Reviewerly/exHarmony huggingface dataset:
A Novel Approach of IoT Stream Sampling and Model Update on the IoT Edge Device for Class Incremental Learning in an Edge-Cloud System

With the exponential rise of the number of IoT devices, the amount of data being produced is massive. Thus, it is unfeasible to send all the raw data directly to the cloud for processing, especially for data that is high dimensional. Training deep learning models incrementally evolves the model over time and eliminates the need to statically training the models with all the data. However, the integration of class incremental learning and the Internet of Things (IoT) is a new concept and is not yet mature. In the context of IoT and deep learning, the transmission cost of data in the edge-cloud architecture is a challenge. We demonstrate a novel sample selection method that discards certain training images on the IoT edge device that reduces transmission cost and still maintains class incremental learning performance. It can be unfeasible to transmit all parameters of a trained model back to the IoT edge device. Therefore, we propose an algorithm to find only the useful parameters of a trained model in an efficient way to reduce the transmission cost from the cloud to the edge devices. Results show that our proposed methods can effectively perform class-incremental learning in an edge-cloud setting.
Local Stability of Wasserstein GANs With Abstract Gradient Penalty

The convergence of generative adversarial networks (GANs) has been studied substantially in various aspects to achieve successful generative tasks. Ever since it is first proposed, the idea has achieved many theoretical improvements by injecting an instance noise, choosing different divergences, penalizing the discriminator, and so on. In essence, these efforts are to approximate a real-world measure with an idle measure through a learning procedure. In this article, we provide an analysis of GANs in the most general setting to reveal what, in essence, should be satisfied to achieve successful convergence. This work is not trivial since handling a converging sequence of an abstract measure requires a lot more sophisticated concepts. In doing so, we find an interesting fact that the discriminator can be penalized in a more general setting than what has been implemented. Furthermore, our experiment results substantiate our theoretical argument on various generative tasks.
New Wilson-like theorems arising from Dickson polynomials

Wilson's Theorem states that the product of all nonzero elements of a finite field ${\mathbb F}_q$ is $-1$. In this article, we define some natural subsets $S \subset {\mathbb F}_q^\times$ and find formulas for the product of the elements of $S$, denoted $\prod S$. These new formulas are appealing for the simple, natural description of the sets $S$, and for the simplicity of the product. An example is $\prod\left\{ a \in {\mathbb F}_q^\times : \text{$1-a$ and $3+a$ are nonsquares} \right\} = 2$ if $q \equiv \pm 1 \pmod{12}$, or $-1$ otherwise.
A Bottom-up Framework for Construction of Structured Semantic 3D Scene Graph

For high-level human-robot interaction tasks, 3D scene understanding is important and non-trivial for autonomous robots. However, parsing and utilizing effective environment information of the 3D scene is not trivial due to the complexity of the 3D environment and the limited ability for reasoning about our visual world. Although there have been great efforts on semantic detection and scene analysis, the existing solutions for parsing and representation of the 3D scene still fail to preserve accurate semantic information and equip sufficient applicability. This study proposes a bottomup construction framework for structured 3D scene graph generation, which efficiently describes the objects, relations and attributes of the 3D indoor environment with structured representation. In the proposed method, we adopt visual perception to capture the semantic information and inference from scene priors to calculate the optimal parse graph. Afterwards, an improved probabilistic grammar model is used to represent the scene priors. Experiment results demonstrate that the proposed framework significantly outperforms existing methods in terms of accuracy, and a demonstration is provided to verify the applicability in applying to high-level human-robot interaction tasks. The supplementary video can be accessed at the following link: https://youtu.be/vEWNxnSwmKI.
Action Recognition Based on C3D Network and Adaptive Keyframe Extraction

In this paper, a video action recognition method is developed based on the C3D neural network and the support vector machine. Rather than directly input the considered video clips to the network, an novel adaptive keyframe extraction strategy is proposed to first extract keyframes which are then input to the network. In our keyframe extraction strategy, whether or not the keyframe extraction procedure should be implemented is determined by the extent of information redundancy existed in the video clips and measured in terms of the scene change speed detected based on an RGB histogram. Keyframes are extracted with an adaptive and iterative scheme based on optical flow analysis. With keyframes as input, a C3D convolutional neural network is trained for feature extraction which are then input to a trained support vector machine for behavior recognition. Experimental results show that with the adaptive keyframe extraction scheme involved, the performance of the action recognition method is improved significantly in the UCF101 dataset.
Advancement in Precision Medicine and Recommendation System for Clinical Trials Using Deep Learning Methods

Abstract The arena for precision medicine has made vast development in progress with big data, deep learning. The personalized health information provides more insight on patient care in all directions which gives better treatment. Many researchers and peoples accept personalized provides more quality of diagnosis and medicine. This paper provides an overview various methods, algorithms, frameworks developed for personalized healthcare. In this article focus on two main accept one is recommendation system for personalized healthcare which provides more data insight mechanism and approach to diagnosis a patient, second it focus deep learning mechanisms in various fields of healthcare, bioinformatics and genomics to deliver accurate results based on advancements in algorithm. This article reveals both combination of recommendation system along with deep learning quality of precision healthcare achieved to patients.
Visual Analysis of Multi-Parameter Distributions Across Ensembles of 3D Fields

For an ensemble of 3D multi-parameter fields, we present a visual analytics workflow to analyse whether and which parts of a selected multi-parameter distribution is present in all ensemble members. Supported by a parallel coordinate plot, a multi-parameter brush is applied to all ensemble members to select data points with similar multi-parameter distribution. By a combination of spatial sub-division and a covariance analysis of partitioned sub-sets of data points, a tight partition in multi-parameter space with reduced number of selected data points is obtained. To assess the representativeness of the selected multi-parameter distribution across the ensemble, we propose a novel extension of violin plots that can show multiple parameter distributions simultaneously. We investigate the visual design that effectively conveys (dis-)similarities in multi-parameter distributions, and demonstrate that users can quickly comprehend parameter-specific differences regarding distribution shape and representativeness from a side-by-side view of these plots. In a 3D spatial view, users can analyse and compare the spatial distribution of selected data points in different ensemble members via interval-based isosurface raycasting. In two real-world application cases we show how our approach is used to analyse the multi-parameter distributions across an ensemble of 3D fields.
Using Machine Learning to Examine Cyberattack Motivations on Web Defacement Data

Social scientists have long been interested in the motives of hackers, particularly financially motivated attackers. This article analyzes web defacements, a less studied and more public form of cyberattack, in which the content of a web page is deliberately substituted with unwanted text and graphics chosen by the perpetrator. These attacks use a variety of strategies and are performed for a variety of motives, including political and ideological goals. The proliferation of such attacks has resulted in vast amounts of data that open new opportunities for qualitative and quantitative analysis. This article explores the usefulness of machine learning techniques to better understand attacker strategies and motivations. To detect overall attack patterns, this analysis utilized a sample of 40,000 images posted on defaced websites analyzed through deep machine learning methods. The approach demonstrates the potential of machine learning approaches for the study of cyberattacks, but it also reveals the considerable challenges that need to be overcome.
Research on Adopting Artificial Intelligence Technology to Improve Effectiveness of Vocational College English Learning

Abstract In the traditional English learning mode of vocational college, teachers and students spend a lot of time on the basic knowledge of English. Due to the singularity of teaching methods and the lack of effective student self-learning platform, many students consider that English learning is boring and the actual learning effect is poo r[1-2] . As an emerging edge discipline, artificial intelligence covers a series of topics such as image recognition, pattern recognition, robotics, natural language understanding and expert systems. The simulation and application of human language intelligence is an essential progress of artificial intelligence technology [3] . This paper will analyze the connotation of artificial intelligence technology and its principles of natural language understanding, and sort out the application advantages of artificial intelligence in education field. Finally, it puts forward practical measures of adopting artificial intelligence tools in input and output stages of English language learning [4-5] .
Efficient, Actively Secure MPC with a Dishonest Majority: A Survey

The last ten years have seen a tremendous growth in the interest and practicality of secure multiparty computation (MPC) and its possible applications. Secure MPC is indeed a very hot research topic and recent advances in the field have already been translated into commercial products world-wide. A major pillar in this advance has been in the case of active security with a dishonest majority, mainly due to the SPDZ-line of work protocols. This survey gives an overview of these protocols, with a focus of the original SPDZ paper (Damgård et al. CRYPTO 2012) and its subsequent optimizations.
A Network for Detecting Facial Features During the COVID-19 Epidemic

The new coronavirus can spread through respiratory droplets, and wearing a mask correctly can effectively prevent the virus from spreading. However, the current detection algorithms are based on unobstructed faces, which affects the detection task when wearing a mask. To solve these problems, a facial feature detection algorithm based on Mtcnn+Mobilenet+GDBT in complex scenes is proposed. First, it can detect whether to wear a mask and the fatigue state of the face. Second, it can set different thresholds according to the facial characteristics of different people, and initialize the characteristics of different frames in 5 seconds. The innovation of our paper: the self-adaption characteristics for every person, it avoids measuring everyone by one standard, which is of great significance to the popularization of the product. Then train a dataset of masks and feature points containing 708 images. The experimental results show that compared with the traditional detection network, the new network can effectively detect facial features in the context of the epidemic. The loss we adopt is Focal loss. The lowest loss of net is 0.01 nearly.
Self-Supervised Learning of Domain-Invariant Local Features for Robust Visual Localization Under Challenging Conditions

Visual localization provides the basis for many robotics applications such as autonomous navigation or augmented reality. Especially in outdoor scenes, robust localization requires local features which can be reliably extracted and matched under changing conditions. Previous approaches have applied generative image-to-image translation models to align images in a single domain before correspondence search. In this letter, we invert the concept and elaborate why it is more promising to use image domain adaptation for training of robust local features. Integrating this idea into a self-supervised training framework, we show in various experiments covering image matching, visual localization, and scene reconstruction that our Domain-Invariant SuperPoint (DISP) outperforms existing self-supervised methods in terms of repeatability, generalization, and robustness. In contrast to competitive supervised local features, our modular and fully self-supervised approach can be easily adapted to different domains and localization tasks as it does not require ground truth correspondences for training.
Online BayesSim for Combined Simulator Parameter Inference and Policy Improvement

Recent advancements in Bayesian likelihood-free inference enables a probabilistic treatment for the problem of estimating simulation parameters and their uncertainty given sequences of observations. Domain randomization can be performed much more effectively when a posterior distribution provides the correct uncertainty over parameters in a simulated environment. In this paper, we study the integration of simulation parameter inference with both model-free reinforcement learning and model-based control in a novel sequential algorithm that alternates between learning a better estimation of parameters and improving the controller. This approach exploits the interdependence between the two problems to generate computational efficiencies and improved reliability when a black-box simulator is available. Experimental results suggest that both control strategies have better performance when compared to traditional domain randomization methods.
Wearable Crazy Eights

Participatory design with wearable users entails engaging people in the design process from the early ideation phases. However, user-generated wearable concepts are often limited by the narrow design space of commercially available wearables. This paper presents an ideation scaffolding method we developed for eliciting wearable concepts, called Wearable Crazy Eights, where participants used an ideation deck and sketched up to 8 concepts in 8 minutes. Herein, we discuss the artifacts produced from our ideation method in a study with 46 participants comparing 3 groups. By comparing the 3 groups we were able to parse the effects of each activity on the resulting ideas. Our contribution is a replicable and customizable ideation method for encouraging outside-the-box thinking in wearable studies.
Control In Agile IS Development Projects: Looking Beyond Agency Theory

Information systems development (ISD) project control has been traditionally seen as a tool against agency concerns, i.e. to align the interests of project participants with the organization’s interests. However, this perspective barely fits into the agile worldview. Agile methods, that became popular choices for ISD projects, shifted away from the commander role of project leaders, and propagate the importance of collaboration and facilitation. Therefore, it became ambiguous why project managers of agile ISD projects engage in control activities. This qualitative study employs the “Gioia method” to investigate managerial reasons for these exercises and draws attention to the range of problems and needs these procedures can address. The analysis found that control mechanisms are enacted not only due to agency concerns, but also to enhance cooperation and communication, to signal to senior management and to become isomorphic with other projects.
A novel 2D/3D hierarchical registration framework via principal-directional Fourier transform operator

Abstract An effective registration framework between preoperative 3D computed tomography and intraoperative 2D x-ray images is crucial in image-guided therapy. In this paper, a novel 2D/3D hierarchical registration framework via principal-directional Fourier transform operator (HRF-PDFTO) is proposed. First, a PDFTO was established to obtain the in-plane translation and rotation invariance. Then, an initial free template-matching approach based on PDFTO was utilized to avoid initial value assignment and expand the capture range of registration. Finally, the hierarchical registration framework, HRF-PDFTO, was proposed to reduce the dimensions of the registration search space from n 6 to n 2 . The experimental results demonstrated that the proposed HRF-PDFTO has good performance with an accuracy of 0.72 mm, and a single registration time of 16 s, which improves the registration efficiency by ten times. Consequently, the HRF-PDFTO can meet the accuracy and efficiency requirements of 2D/3D registration in related clinical applications.
What's Going Ön?

Citizen participation in urban planning has become more important in the past decades. In recent years, the practice of co-creation has become a popular tool to structure this participatory process. Co-creation is a process that actively involves stakeholders in cooperatively sharing and developing opinions and perspectives towards an outcome that contributes to the overall development. In particular in large and slow processes such as urban planning, co-creation risks becoming abstract and difficult to engage with for stakeholders. This study investigates an experiential approach to participation in urban planning. By building on embodiment to engage participants physically and expressively, this study aims to elicit an understanding between different points of view. With the design of the "What's going Ön?" experience we explore how Virtual Reality (VR) can play a role in the participation of citizens in the urban planning process of the Ön island in Umeå, Sweden. We present the designed experience and the qualities that play a role in using VR as a tool for participation in Urban Planning.
Knowledge-aware multi-center clinical dataset adaptation: Problem, method, and application

• To adaptable utilize clinical data collected from multiple centers for potential clinical applications. • To propose an adversarial learning model to capture shared patient feature representations from heterogeneous datasets. • To employ an external KG to enrich the semantics of the patient sample. • Evaluation is done using two real datasets for the task of predicting acute kidney injury in patients with heart failure. Adaptable utilization of clinical data collected from multiple centers, prompted by the need to overcome the shifts between the dataset distributions, and exploit these different datasets for potential clinical applications, has received significant attention in recent years. In this study, we propose a novel approach to this task by infusing an external knowledge graph (KG) into multi-center clinical data mining. Specifically, we propose an adversarial learning model to capture shared patient feature representations from multi-center heterogeneous clinical datasets, and employ an external KG to enrich the semantics of the patient sample by providing both clinical center-specific and center-general knowledge features, which are trained with a graph convolutional autoencoder. We evaluate the proposed model on a real clinical dataset extracted from the general cardiology wards of a Chinese hospital and a well-known public clinical dataset (MIMIC III, pertaining to ICU clinical settings) for the task of predicting acute kidney injury in patients with heart failure. The achieved experimental results demonstrate the efficacy of our proposed model.
Self-Supervised Attention Learning for Depth and Ego-motion Estimation

We address the problem of depth and ego-motion estimation from image sequences. Recent advances in the domain propose to train a deep learning model for both tasks using image reconstruction in a self-supervised manner. We revise the assumptions and the limitations of the current approaches and propose two improvements to boost the performance of the depth and ego-motion estimation. We first use Lie group properties to enforce the geometric consistency between images in the sequence and their reconstructions. We then propose a mechanism to pay attention to image regions where the image reconstruction gets corrupted. We show how to integrate the attention mechanism in the form of attention gates in the pipeline and use attention coefficients as a mask. We evaluate the new architecture on the KITTI datasets and compare it to the previous techniques. We show that our approach improves the state-of-the-art results for ego-motion estimation and achieve comparable results for depth estimation.
Understand growth episodes in Sub-Saharan Africa: Do exogenous shocks matters?

This paper analyzes the effects of exogenous shocks on long-term growth. Additionally, it analyzes episodes of growth acceleration and reversal in 33 sub-Saharan Africa countries over the period 1980–2016. Fixed effects and the generalized method of moments are used to assess the effects of exogenous shocks on long-term growth. The results show that the terms of trade, remittances, and world demand improve long-term growth, while aid reduces it. Growth acceleration and reversal episodes are identified using an improved variant of the filter developed by Hausmann et al. (2005. "Growth Accelerations." Journal of Economic Growth 10: 303–329). We further used a probit model to evaluate how exogenous shocks affect both growth episodes. Our results show that a favorable shock on the terms of trade, foreign aid and world demand increases the probability of acceleration, while an unfavorable shock on the terms of trade, remittances and world demand increases the probability of a growth reversal. Moreover, we find that economic and political reforms precede growth accelerations. Finally, the results show that growth reversals are associated with higher inflation, lower domestic credit, and civil wars.
DEDPUL: Difference-of-Estimated-Densities-based Positive-Unlabeled Learning

Positive-Unlabeled (PU) learning is an analog to supervised binary classification for the case when only the positive sample is clean, while the negative sample is contaminated with latent instances of positive class and hence can be considered as an unlabeled mixture. The objectives are to classify the unlabeled sample and train an unbiased positive-negative classifier, which generally requires to identify the mixing proportions of positives and negatives first. Recently, unbiased risk estimation framework has achieved state-of-the-art performance in PU learning. This approach, however, exhibits two major bottlenecks. First, the mixing proportions are assumed to be identified, i.e. known in the domain or estimated with additional methods. Second, the approach relies on the classifier being a neural network. In this paper, we propose DEDPUL, a method that solves PU Learning without the aforementioned issues. The mechanism behind DEDPUL is to apply a computationally cheap postprocessing procedure to the predictions of any classifier trained to distinguish positive and unlabeled data. Instead of assuming the proportions to be identified, DEDPUL estimates them alongside with classifying unlabeled sample. Experiments show that DEDPUL outperforms the current state-of-the-art in both proportion estimation and PU Classification and is flexible in the choice of the classifier.