Reviewerly/exHarmony huggingface dataset:
Fall prediction based on key points of human bones

With the development of society, the number of old people is increasing. Slow response, osteoporosis and vision loss threaten the health of the elderly. The fall of this problem is an important factor that threatens the health of the elderly. In order to reduce the damage caused by falls, this paper based on the human skeleton map for fall prediction. First using OPENPOSE get the bone map and make it into a data set. Then using transfer learning to train the data set to get a new model Finally, the new model is used to predict the fall. The innovations in this paper are to take bone maps from 2D images and use bone maps to make fall predictions. The bone map is predicted using a convolutional neural network. The final experimental results show that the new model obtained through transfer learning has an accuracy rate of 91.7%. This result fully demonstrates the validity of the proposed model.
Evaluating the Impact of Virtual Reality on Design Review Meetings

With the help of virtual reality (VR), architectural design can be visualized interactively and at full scale. Viewers of VR models also gain an additional sense of spatial depth and volume. These capabilities make VR a unique design visualization media, and have attracted research interests in applying VR for design reviews. For example, previous research has studied the application of VR for evaluating the designs of courtrooms, nuclear power plants, patient rooms, and educational buildings. This research extends contributions reported in existing literature that aims to explore the value and impact of VR on design review meetings. This research performs a detailed analysis of a large number of design review meetings, including 13 meetings conducted on building construction projects. A novel design review evaluation methodology, designed to leverage video analysis techniques was adopted to allow for higher consistency of the evaluation results and reducing the impact of user bias caused by the novelty effect of VR. This methodology provides systematic rules and metrics to analyze design review meeting quality. The research has also extended the analysis of the impact of VR-based design review meetings to those that involve occupants and facility maintenance staff, which is a type of VR-based review that has not been extensively studied in the previous literature. The results of this research show that VR alone is not necessarily better than traditional media, such as drawings, in facilitating active discussions of design issues. Instead, the authors find that design review meetings yield more effective discussion and better meeting efficiency when VR is orchestrated with, and supported by, other design visualization media, such as drawings, digital models, renderings, and maps. This paper also summarizes six strengths and four weaknesses of VR to support design review meetings when compared with 2D drawings. To help project teams decide if VR should be used for their design reviews, the authors recommend the consideration of three criteria: (1) the reviewers’ exposure to the design, (2) the scope of the review, and (3) the project complexity.
Task engagement, learner motivation and avatar identities of struggling English language learners in the 3D virtual world

This study investigated at-risk ESL learners' task performance and attitudes towards a 3D approach of improving their English spoken and written communication skills in Second Life (SL). It documented how these SL “newbies,” who had not previously experienced 3D virtual learning, carried out SL-enabled, real-life oriented and interactive tasks. Qualitative data were triangulated from students’ weekly blog entries, reflective essay writing and a focus group interview, followed by a thematic analysis approach. Despite the technical glitches impeding the flow of virtual class management and causing communication breakdown, students overall held positive perceptions of the task-based design implemented in SL. Unique SL affordances were found to maximise task execution and engagement, thus fostering more authentic and crosscultural communication, building confidence, boosting motivation, and empowering learners via avatar anonymity. The game factor also transformed a conventional English class into a fun virtual learning playground. Such findings not only highlight pedagogical suggestions for curriculum design and language program improvement, but also implicate both challenges and possibilities of conducting research and teaching at-risk language learners in a 3D sphere.
DistInit: Learning Video Representations Without a Single Labeled Video

Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models has not been able to keep up with the ever increasing depth and sophistication of these networks. In this work we propose an alternative approach to learning video representations that requires no semantically labeled videos, and instead leverages the years of effort in collecting and labeling large and clean still-image datasets. We do so by using state-of-the-art models pre-trained on image datasets as “teachers” to train video models in a distillation framework. We demonstrate that our method learns truly spatiotemporal features, despite being trained only using supervision from still-image networks. Moreover, it learns good representations across different input modalities, using completely uncurated raw video data sources and with different 2D teacher models. Our method obtains strong transfer performance, outperforming standard techniques for bootstrapping video architectures with image based models by 16%. We believe that our approach opens up new approaches for learning spatiotemporal representations from unlabeled video data.
3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers

We tackle the problem of automatically reconstructing a complete 3D model of a scene from a single RGB image. This challenging task requires inferring the shape of both visible and occluded surfaces. Our approach utilizes viewer-centered, multi-layer representation of scene geometry adapted from recent methods for single object shape completion. To improve the accuracy of view-centered representations for complex scenes, we introduce a novel "Epipolar Feature Transformer" that transfers convolutional network features from an input view to other virtual camera viewpoints, and thus better covers the 3D scene geometry. Unlike existing approaches that first detect and localize objects in 3D, and then infer object shape using category-specific models, our approach is fully convolutional, end-to-end differentiable, and avoids the resolution and memory limitations of voxel representations. We demonstrate the advantages of multi-layer depth representations and epipolar feature transformers on the reconstruction of a large database of indoor scenes.
A time-varying mirrored S-shaped transfer function for binary particle swarm optimization

Binary Particle swarm optimization (BPSO) is one of the most popular swarm intelligence algorithms to solve binary optimization problems. It has a few parameters, simple structure, and high execution speed. A transfer function is applied in BPSO to convert the continuous search space to the binary one. This algorithm and its variants can sometimes find local optima or exhibit slow convergence speed. Thus, many researchers have improved the structure of BPSO and its transfer function to overcome these shortcomings. In this study, a new time-varying mirrored S-shaped transfer function for BPSO (TVMS-BPSO) is introduced to enhance global exploration and local exploitation in the algorithm. The performance of the proposed transfer function has been compared with some well-known BPSO algorithms and binary meta-heuristic algorithms. These algorithms have been evaluated by CEC 2005 benchmark functions and set of 0–1 multidimensional knapsack problem (MKP) benchmark instances. The experimental results showed that the new transfer function significantly enhances the efficiency of BPSO for both local and global topologies in terms of solution accuracy and convergence speed.
Object Detection and Instance Segmentation in Remote Sensing Imagery Based on Precise Mask R-CNN

Object detection in very high-resolution (VHR) remote sensing images is a fundamental and challenging problem due to the complex environments. In this paper, a precise mask region convolutional neural network (precise Mask R-CNN) is presented for object detection and instance segmentation in VHR remote sensing images. This method generates bounding boxes and segmentation masks for each instance of an object in the image. Contrary to regions of interest (RoI) Align whose sample points is pre-defined and not adaptive the size of the bin, the proposed precise RoI pooling can directly compute the two-order integral based on the continuous feature map to avoid loss of precision. The experiments on NWPU VHR-10 dataset show that the presented precise Mask R-CNN improves the accuracy of object detection and instance segmentation for VHR remote sensing images. Furthermore, it promotes the application of instance segmentation in VHR remote sensing.
Dynamic Anchor Feature Selection for Single-Shot Object Detection

The design of anchors is critical to the performance of one-stage detectors. Recently, the anchor refinement module (ARM) has been proposed to adjust the initialization of default anchors, providing the detector a better anchor reference. However, this module brings another problem: all pixels at a feature map have the same receptive field while the anchors associated with each pixel have different positions and sizes. This discordance may lead to a less effective detector. In this paper, we present a dynamic feature selection operation to select new pixels in a feature map for each refined anchor received from the ARM. The pixels are selected based on the new anchor position and size so that the receptive filed of these pixels can fit the anchor areas well, which makes the detector, especially the regression part, much easier to optimize. Furthermore, to enhance the representation ability of selected feature pixels, we design a bidirectional feature fusion module by combining features from early and deep layers. Extensive experiments on both PASCAL VOC and COCO demonstrate the effectiveness of our dynamic anchor feature selection (DAFS) operation. For the case of high IoU threshold, our DAFS can improve the mAP by a large margin.
Encrypted Databases

We present a range of novel attacks which exploit information about the volume of answers to range queries in encrypted database. Our attacks rely on a strategy which is simple yet robust and effective. We illustrate the robustness of our strategy in a number of ways. We show how i) to adapt the attack for several variations of a basic usage scenario ii) to defeat countermeasures intended to thwart the premise of our basic attack and iii) to perform partial reconstruction of secret data when unique reconstruction is information theoretically impossible. Furthermore, over the state of the art, our attacks require one order of magnitude fewer queries. We show how to improve the attacks even further, under the assumption that some partial information is known to the adversary. We validate experimentally all of our attacks through extensive experiments on real-world medical data and justify theoretically the effectiveness of our strategy for the basic attack scenario. Our new attacks further underscore the difficulty of striking an appropriate functionality-security trade-off for encrypted databases.
Weighted and Class-Specific Maximum Mean Discrepancy for Unsupervised Domain Adaptation

Although maximum mean discrepancy (MMD) has achieved great success in unsupervised domain adaptation (UDA), most of existing UDA methods ignore the issue of class weight bias across domains, which is ubiquitous and evidently gives rise to the degradation of UDA performance. In this work, we propose two improved MMD metrics, i.e., weighted MMD (WMMD) and class-specific MMD (CMMD), to alleviate the adverse effect caused by the changes of class prior distributions between source and target domains. In WMMD, class-specific auxiliary weights are deployed to reweigh the source samples. In CMMD, we calculate the MMD for each class of source and target samples. Since the class labels of target samples are unknown for UDA problem, we present a classification expectation-maximization algorithm to estimate the pseudo-labels of target samples on the fly and update the model parameters using estimated labels. The proposed methods can be flexibly incorporated into deep convolutional neural networks to form WMMD and CMMD based domain adaptation networks, which we called WDAN and CDAN, respectively. By combining WMMD with CMMD, we present a CWMMD based domain adaptation network (CWDAN) to further improve classification performance. Experiments show that, both WMMD and CMMD benefit the classification accuracy, and our CWDAN can achieve compelling UDA performance in comparison with MMD and the state-of-the-art UDA methods.
Feature agglomeration networks for single stage face detection

Recent years have witnessed promising results of exploring deep convolutional neural network for face detection. Despite making remarkable progress, face detection in the wild remains challenging especially when detecting faces at vastly different scales and characteristics. In this paper, we propose a novel simple yet effective framework of "Feature Agglomeration Networks" (FANet) to build a new single-stage face detector, which not only achieves state-of-the-art performance but also runs efficiently. As inspired by Feature Pyramid Networks (FPN) (Lin et al., 2017), the key idea of our framework is to exploit inherent multi-scale features of a single convolutional neural network by aggregating higher-level semantic feature maps of different scales as contextual cues to augment lower-level feature maps via a hierarchical agglomeration manner at marginal extra computation cost. We further propose a Hierarchical Loss to effectively train the FANet model. We evaluate the proposed FANet detector on several public face detection benchmarks, including PASCAL face, FDDB, and WIDER FACE datasets and achieved state-of-the-art results2. Our detector can run in real-time for VGA-resolution images on GPU.
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots

In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task. First, IMN constructs word representations from three aspects to address the challenge of out-of-vocabulary (OOV) words. Second, an attentive hierarchical recurrent encoder (AHRE), which is capable of encoding sentences hierarchically and generating more descriptive representations by aggregating with an attention mechanism, is designed. Finally, the bidirectional interactions between whole multi-turn contexts and response candidates are calculated to derive the matching information between them. Experiments on four public datasets show that IMN outperforms the baseline models on all metrics, achieving a new state-of-the-art performance and demonstrating compatibility across domains for multi-turn response selection.
Context information sharing for the Internet of Things: A survey

Internet of Things (IoT) technology is starting to make an impact in a wide array of applications, including smart cities and industrial environments. Such real-world applications combine computation, communication, sensing, and in some cases, actuation, to monitor and remotely control the environment. Data is at the core of such real-world IoT applications. Analysis, modeling, and reasoning of data are necessary to gain valuable insights. Application developers employ context-aware systems to translate the data into contextual information, which then allows the applications to act cognitively. Context sharing platforms offer a solution to distribute context information to those who may be interested in it, thus enabling context interoperability among different entities. This survey first examines the requirements for sharing context information. It then reviews the relevant literature for context sharing and classifies them based on their requirements and characteristics. Challenges and future directions are presented to encourage the development of context sharing platforms.
Server-Aided Attribute-Based Signature Supporting Expressive Access Structures for Industrial Internet of Things

Existing server-aided attribute-based signature (SA-ABS) to secure industrial Internet of Things (IIoT)-oriented applications raise challenges such as achieving collusion attack resilience and realizing expressive linear secret-sharing scheme (LSSS) access structures. In this paper, we positively address these challenges by proposing a novel SA-ABS for IIoT. Distinct from the existing works in this field, our SA-ABS protocol not only allows to delegate the heavy calculation in both signature generation and verification to a third-party server, but also resists the collusion attack and realizes expressive LSSS access structures. The formal proof about the unforgeability of the proposed SA-ABS has been given on the basis of the standard model. Theoretical analysis as well as experimental simulation reveal the fact that the proposed SA-ABS is feasible and efficient.
An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

In recent times, selection of a suitable hotel location and reservation of accommodation have become a critical issue for the travelers. The online hotel search has been increased at a very fast pace and became very time-consuming due to the presence of huge amount of online information. Recommender systems (RSs) are getting importance due to their significance in making decisions and providing detailed information about the required product or a service. To acquire the hotel recommendations while dealing with textual hotel reviews, numerical ranks, votes, ratings, and number of video views have become difficult. To generate true recommendations, we have proposed an intelligent approach which also deals with large-sized heterogeneous data to fulfill the needs of the potential customers. The collaborative filtering (CF) approach is one of the most popular techniques of the RS to generate recommendations. We have proposed a novel CF recommendation approach in which opinion-based sentiment analysis is used to achieve hotel feature matrix by polarity identification. Our approach combines lexical analysis, syntax analysis, and semantic analysis to understand sentiment towards hotel features and the profiling of guest type (solo, family, couple etc). The proposed system recommends hotels based on the hotel features and guest type for personalized recommendation. The developed system not only has the ability to handle heterogeneous data using big data Hadoop platform but it also recommends hotel class based on guest type using fuzzy rules. Different experiments are performed over the real-world datasets obtained from two hotel websites. Moreover, the values of precision and recall and F-measure have been calculated, and the results are discussed in terms of improved accuracy and response time, significantly better than the traditional approaches.
An energy, performance efficient resource consolidation scheme for heterogeneous cloud datacenters

Datacenters are the principal electricity consumers for cloud computing that provide an IT backbone for today's business and economy. Numerous studies suggest that most of the servers, in the US datacenters, are idle or less-utilised, making it possible to save energy by using resource consolidation techniques. However, consolidation involves migrations of virtual machines, containers and/or applications, depending on the underlying virtualisation method; that can be expensive in terms of energy consumption and performance loss. In this paper, we: (a) propose a consolidation algorithm which favours the most effective migration among VMs, containers and applications; and (b) investigate how migration decisions should be made to save energy without any negative impact on the service performance. We demonstrate through a number of experiments, using the real workload traces for 800 hosts, approximately 1516 VMs, and more than million containers, how different approaches to migration, will impact on datacenter's energy consumption and performance. We suggest, using reasonable assumptions for datacenter set-up, that there is a trade-off involved between migrating containers and virtual machines. It is more performance efficient to migrate virtual machines; however, migrating containers could be more energy efficient than virtual machines. Moreover, migrating containerised applications, that run inside virtual machines, could lead to energy and performance efficient consolidation technique in large-scale datacenters. Our evaluation suggests that migrating applications could be ~5.5% more energy efficient and ~11.9% more performance efficient than VMs migration. Further, energy and performance efficient consolidation is ~14.6% energy and ~7.9% performance efficient than application migration. Finally, we generalise our results using several repeatable experiments over various workloads, resources and datacenter set-ups.
A chaotic path planning generator based on logistic map and modulo tactics

A simple, short and efficient chaotic path planning algorithm is proposed for autonomous mobile robots, with the aim of covering a given terrain using chaotic, unpredictable motion. The proposed technique utilizes the logistic map with a chaotic tactic that utilizes a modulo function to produce a sequence of directions for a robot that can move in eight different directions on a grid. Extensive simulations are performed, and the results show a fast and efficient scanning of the given area. In addition, the proposed algorithm is further enhanced with a pheromone inspired memory technique, with good improvements in efficiency.
Outlier exposure with confidence control for out-of-distribution detection

Deep neural networks have achieved great success in classification tasks during the last years. However, one major problem to the path towards artificial intelligence is the inability of neural networks to accurately detect samples from novel class distributions and therefore, most of the existent classification algorithms assume that all classes are known prior to the training stage. In this work, we propose a methodology for training a neural network that allows it to efficiently detect out-of-distribution (OOD) examples without compromising much of its classification accuracy on the test examples from known classes. We propose a novel loss function that gives rise to a novel method, Outlier Exposure with Confidence Control (OECC), which achieves superior results in OOD detection with OE both on image and text classification tasks without requiring access to OOD samples. Additionally, we experimentally show that the combination of OECC with state-of-the-art post-training OOD detection methods, like the Mahalanobis Detector (MD) and the Gramian Matrices (GM) methods, further improves their performance in the OOD detection task, demonstrating the potential of combining training and post-training methods for OOD detection.