Reviewerly/exHarmony huggingface dataset:
A Novel Human Activity Recognition Scheme for Smart Health Using Multilayer Extreme Learning Machine

In recent years, more and more wearable sensors have been employed in smart health applications. Wearable sensors not only can be used to collect valuable health-related data of their users, they can be also used in conjunction with other infrastructure-bound sensors, such as Microsoft Kinect sensor, to facilitate privacy-aware fine-grained activity tracking. This fusion of multimodal data promises a new type of smart health applications that coach a user to live a healthier life style by monitoring the user in realtime and reminding him or her when he or she engages in an unhealthy activity. In this paper, we investigate how to achieve fine-grained activity recognition in the context of such an application. In our scheme, the identification accuracy is improved by incorporating a nonlinear and local similarity measure, namely kernel risk-sensitive loss, into a novel multilayer neural network learning algorithm, called as stacked extreme learning machine. Furthermore, to achieve a good generalization performance with minimal human intervention, Jaya as a popular optimization algorithm, is also used to adjust key parameters in our proposed approach. The experiments are conducted to verify the effectiveness of the proposed scheme.
Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks

There are many reasons to expect an ability to reason in terms of objects to be a crucial skill for any generally intelligent agent. Indeed, recent machine learning literature is replete with examples of the benefits of object-like representations: generalization, transfer to new tasks, and interpretability, among others. However, in order to reason in terms of objects, agents need a way of discovering and detecting objects in the visual world - a task which we call unsupervised object detection. This task has received significantly less attention in the literature than its supervised counterpart, especially in the case of large images containing many objects. In the current work, we develop a neural network architecture that effectively addresses this large-image, many-object setting. In particular, we combine ideas from Attend, Infer, Repeat (AIR), which performs unsupervised object detection but does not scale well, with recent developments in supervised object detection. We replace AIR’s core recurrent network with a convolutional (and thus spatially invariant) network, and make use of an object-specification scheme that describes the location of objects with respect to local grid cells rather than the image as a whole. Through a series of experiments, we demonstrate a number of features of our architecture: that, unlike AIR, it is able to discover and detect objects in large, many-object scenes; that it has a significant ability to generalize to images that are larger and contain more objects than images encountered during training; and that it is able to discover and detect objects with enough accuracy to facilitate non-trivial downstream processing.
Motion Guided Spatial Attention for Video Captioning

Sequence-to-sequence models incorporated with attention mechanism have shown promising improvements on video captioning. While there is rich information both inside and between frames, spatial attention is rarely explored and motion information is usually handled by 3D-CNNs as just another modality for fusion. On the other hand, researches about human perception suggest that apparent motion can attract attention. Motivated by this, we aim to learn spatial attention on video frames under the guidance of motion information for caption generation. We present a novel video captioning framework by utilizing Motion Guided Spatial Attention (MGSA). The proposed MGSA exploits the motion between video frames by learning spatial attention from stacked optical flow images with a custom CNN. To further relate the spatial attention maps of video frames, we designed a Gated Attention Recurrent Unit (GARU) to adaptively incorporate previous attention maps. The whole framework can be trained in an end-to-end manner. We evaluate our approach on two benchmark datasets, MSVD and MSR-VTT. The experiments show that our designed model can generate better video representation and state of the art results are obtained under popular evaluation metrics such as BLEU@4, CIDEr, and METEOR.
Obstacles and features of Farm Management Information Systems: A systematic literature review

Various Farm Management Information Systems (FMISs) have been developed to support the management of the farm businesses. These FMISs typically support the different domains of the agricultural sector, such as arable and dairy farming; and include different set of features, such as crop, field, and financial management. These FMISs also have to deal with diverse obstacles during their development and adoption, such as lack of standardized data, cost and usability. Though several papers have been published in the past several years on this topic, there has been no explicit attempt to systematically review these papers to identify and characterize the features and obstacles. The objective of this study is to identify and describe the state-of-the-art of FMISs and as such pave the way for further research and development of FMISs. We applied a systematic literature review protocol in which we included the literature published from 2008 to 2018. We found 1048 papers of which 38 papers were selected as primary studies that we analyzed further in detail. From the detailed analysis, we identified 81 unique FMIS features and 51 unique obstacles of FMISs. We have systematically ranked the identified features and obstacles and describe the key associated aspects. These aspects include the agricultural domains, modeling approaches, delivery models, and identified stakeholders.
Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms

Mammography is successfully used as an effective screening tool for cancer diagnosis. A calcification cluster on mammography is a primary sign of cancer. Early researches have proved the diagnostic value of the calcification, yet their performance is highly dependent on handcrafted image descriptors. Characterizing the calcification mammography in an automatic and robust way remains a challenge. In this paper, the calcification was characterized by descriptors obtained from deep learning and handcrafted descriptors. We compared the performances of different image feature sets on digital mammograms. The feature sets included the deep features alone, the handcrafted features, their combination, and the filtered deep features. Experimental results have demonstrated that the deep features outperform handcrafted features, but the handcrafted features can provide complementary information for deep features. We achieved a classification precision of 89.32% and sensitivity of 86.89% using the filtered deep features, which is the best performance among all the feature sets.
Improving Cloud Gaming Experience through Mobile Edge Computing

With the development of 4G/5G technology and smart devices, more and more users begin to play games via their mobile devices. As a promising way to enable users to play any games, cloud gaming is proposed to stream game scene rendered remotely in the cloud with the format of video. However, it faces major challenges in terms of long delay and high network bandwidth. To this end, a novel framework named EdgeGame is proposed to improve the cloud gaming experience by leveraging resources in the edge. Compared to existing cloud gaming systems, EdgeGame offloads the computation-intensive rendering to the network edge instead, which can reduce network delay and bandwidth consumption greatly. Moreover, EdgeGame introduces deep reinforcement learning in the edge to adjust the video bitrates adaptively to accommodate the network dynamics. Finally, we implemented a prototype system and compared it with an existing cloud gaming system. The experiments show that EdgeGame can reduce the average network delay by 50 percent and improve user's QoE by 20 percent.
Cavity quantum electrodynamics of strongly correlated electron systems: A no-go theorem for photon condensation

In spite of decades of work it has remained unclear whether or not superradiant quantum phases, referred to here as photon condensates, can occur in equilibrium. In this Rapid Communication, we first show that when a nonrelativistic quantum many-body system is coupled to a cavity field, gauge invariance forbids photon condensation. We then present a microscopic theory of the cavity quantum electrodynamics of an extended Falicov-Kimball model, showing that, in agreement with the general theorem, its insulating ferroelectric and exciton condensate phases are not altered by the cavity and do not support photon condensation.
Underwater Image Enhancement Using Adaptive Retinal Mechanisms

We propose an underwater image enhancement model inspired by the morphology and function of the teleost fish retina. We aim to solve the problems of underwater image degradation raised by the blurring and nonuniform color biasing. In particular, the feedback from color-sensitive horizontal cells to cones and a red channel compensation are used to correct the nonuniform color bias. The center-surround opponent mechanism of the bipolar cells and the feedback from amacrine cells to interplexiform cells then to horizontal cells serve to enhance the edges and contrasts of the output image. The ganglion cells with color-opponent mechanism are used for color enhancement and color correction. Finally, we adopt a luminance-based fusion strategy to reconstruct the enhanced image from the outputs of ON and OFF pathways of fish retina. Our model utilizes the global statistics (i.e., image contrast) to automatically guide the design of each low-level filter, which realizes the self-adaption of the main parameters. Extensive qualitative and quantitative evaluations on various underwater scenes validate the competitive performance of our technique. Our model also significantly improves the accuracy of transmission map estimation and local feature point matching using the underwater image. Our method is a single image approach that does not require the specialized prior about the underwater condition or scene structure.
A Neural Multi-digraph Model for Chinese NER with Gazetteers

Gazetteers were shown to be useful resources for named entity recognition (NER) (Ratinov and Roth, 2009).Many existing approaches to incorporating gazetteers into machine learning based NER systems rely on manually defined selection strategies or handcrafted templates, which may not always lead to optimal effectiveness, especially when multiple gazetteers are involved.This is especially the case for the task of Chinese NER, where the words are not naturally tokenized, leading to additional ambiguities.To automatically learn how to incorporate multiple gazetteers into an NER system, we propose a novel approach based on graph neural networks with a multidigraph structure that captures the information that the gazetteers offer.Experiments on various datasets show that our model is effective in incorporating rich gazetteer information while resolving ambiguities, outperforming previous approaches.
BayesSim: Adaptive Domain Randomization Via Probabilistic Inference for Robotics Simulators

We introduce BayesSim 1 , a framework for robotics simulations allowing a full Bayesian treatment for the parameters of the simulator.As simulators become more sophisticated and able to represent the dynamics more accurately, fundamental problems in robotics such as motion planning and perception can be solved in simulation and solutions transferred to the physical robot.However, even the most complex simulator might still not be able to represent reality in all its details either due to inaccurate parametrization or simplistic assumptions in the dynamic models.BayesSim provides a principled framework to reason about the uncertainty of simulation parameters.Given a black box simulator (or generative model) that outputs trajectories of state and action pairs from unknown simulation parameters, followed by trajectories obtained with a physical robot, we develop a likelihood-free inference method that computes the posterior distribution of simulation parameters.This posterior can then be used in problems where Sim2Real is critical, for example in policy search.We compare the performance of BayesSim in obtaining accurate posteriors in a number of classical control and robotics problems.Results show that the posterior computed from BayesSim can be used for domain randomization outperforming alternative methods that randomize based on uniform priors.
Learning Filter Basis for Convolutional Neural Network Compression

Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of images. Usually the success of these methods comes with a cost of millions of parameters due to stacking deep convolutional layers. Moreover, quite a large number of filters are also used for a single convolutional layer, which exaggerates the parameter burden of current methods. Thus, in this paper, we try to reduce the number of parameters of CNNs by learning a basis of the filters in convolutional layers. For the forward pass, the learned basis is used to approximate the original filters and then used as parameters for the convolutional layers. We validate our proposed solution for multiple CNN architectures on image classification and image super-resolution benchmarks and compare favorably to the existing state-of-the-art in terms of reduction of parameters and preservation of accuracy. Code is available at https://github.com/ofsoundof/learning_filter_basis.
LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many different devices and in different data silos. Sharing data among different sources is a big challenge due to regulatory, operational and security reasons. One potential solution is federated machine learning, which is a method that sends machine learning algorithms simultaneously to all data sources, trains models in each source and aggregates the learned models. This strategy allows utilization of valuable data without moving them. One challenge in applying federated machine learning is the possibly different distributions of data from diverse sources. To tackle this problem, we proposed an adaptive boosting method named LoAdaBoost that increases the efficiency of federated machine learning. Using intensive care unit data from hospitals, we investigated the performance of learning in IID and non-IID data distribution scenarios, and showed that the proposed LoAdaBoost method achieved higher predictive accuracy with lower computational complexity than the baseline method.
3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans

We propose 3D Dynamic Scene Graphs (DSGs) as a unified representation for actionable spatial perception.(a) A DSG is a layered and hierarchical representation that abstracts a dense 3D model (e.g., a metric-semantic mesh) into higherlevel spatial concepts (e.g., objects, agents, places, rooms) and models their spatio-temporal relations (e.g., "agent A is in room B at time t", traversability between places or rooms).We present a Spatial PerceptIon eNgine (SPIN) that reconstructs a DSG from visual-inertial data, and (a) segments places, structures (e.g., walls), and rooms, (b) is robust to extremely crowded environments, (c) tracks dense mesh models of human agents in real time, (d) estimates centroids and bounding boxes of objects of unknown shape, (e) estimates the 3D pose of objects for which a CAD model is given.
Hierarchical Graph Network for Multi-hop Question Answering

In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering.To aggregate clues from scattered texts across multiple paragraphs, a hierarchical graph is created by constructing nodes on different levels of granularity (questions, paragraphs, sentences, entities), the representations of which are initialized with pre-trained contextual encoders.Given this hierarchical graph, the initial node representations are updated through graph propagation, and multihop reasoning is performed via traversing through the graph edges for each subsequent sub-task (e.g., paragraph selection, supporting facts extraction, answer prediction).By weaving heterogeneous nodes into an integral unified graph, this hierarchical differentiation of node granularity enables HGN to support different question answering sub-tasks simultaneously.Experiments on the HotpotQA benchmark demonstrate that the proposed model achieves new state of the art, outperforming existing multi-hop QA approaches. 1
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification

Text classification is a critical research topic with broad applications in natural language processing.Recently, graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task.Despite the success, their performance could be largely jeopardized in practice since they are: (1) unable to capture high-order interaction between words; (2) inefficient to handle large datasets and new documents.To address those issues, in this paper, we propose a principled model -hypergraph attention networks (HyperGAT), which can obtain more expressive power with less computational consumption for text representation learning.Extensive experiments on various benchmark datasets demonstrate the efficacy of the proposed approach on the text classification task.
Improvising personalized travel recommendation system with recency effects

A travel recommendation system based on social media activity provides a customized place of interest to accommodate user-specific needs and preferences. In general, the user's inclination towards travel destinations is subject to change over time. In this project, we have analyzed users' twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. The travel tweets are then used to obtain personalized travel recommendations. Unlike most of the personalized recommendation systems, our proposed model takes into account a user's most recent interest by incorporating time-sensitive recency weight into the model. Our proposed model has outperformed the existing personalized place of interest recommendation model, and the overall accuracy is 75.23%.
Anchor DETR: Query Design for Transformer-Based Detector

In this paper, we propose a novel query design for the transformer-based object detection. In previous transformer-based detectors, the object queries are a set of learned embeddings. However, each learned embedding does not have an explicit physical meaning and we cannot explain where it will focus on. It is difficult to optimize as the prediction slot of each object query does not have a specific mode. In other words, each object query will not focus on a specific region. To solve these problems, in our query design, object queries are based on anchor points, which are widely used in CNN-based detectors. So each object query focuses on the objects near the anchor point. Moreover, our query design can predict multiple objects at one position to solve the difficulty: ``one region, multiple objects''. In addition, we design an attention variant, which can reduce the memory cost while achieving similar or better performance than the standard attention in DETR. Thanks to the query design and the attention variant, the proposed detector that we called Anchor DETR, can achieve better performance and run faster than the DETR with 10x fewer training epochs. For example, it achieves 44.2 AP with 19 FPS on the MSCOCO dataset when using the ResNet50-DC5 feature for training 50 epochs. Extensive experiments on the MSCOCO benchmark prove the effectiveness of the proposed methods. Code is available at https://github.com/megvii-research/AnchorDETR.
Global optimum-based search differential evolution

In this paper, a global optimum-based search strategy is proposed to alleviate the situation that the differential evolution (DE) usually sticks into a stagnation, especially on complex problems. It aims to reconstruct the balance between exploration and exploitation, and improve the search efficiency and solution quality of DE. The proposed method is activated by recording the number of recently consecutive unsuccessful global optimum updates. It takes the feedback from the global optimum, which makes the search strategy not only refine the current solution quality, but also have a change to find other promising space with better individuals. This search strategy is incorporated with various DE mutation strategies and DE variations. The experimental results indicate that the proposed method has remarkable performance in enhancing search efficiency and improving solution quality.
Potential Risks of Hyperledger Fabric Smart Contracts

Blockchain is a decentralized ledger technology, and it is the technology underlying Bitcoin and Ethereum. The interest in blockchain has been increasing since its emergence. Hyperledger Fabric is one of the permissioned blockchain frameworks. One of the characteristics of Hyperledger Fabric is it utilizes general-purpose programming languages, e.g., Go, Node.js, and Java, to implement smart contracts (called chaincode in Hyperledger Fabric). The advantages of utilizing these languages are already known to potential developers, and development tools might already exist. However, one of the disadvantages is that these languages were not originally designed for writing smart contracts. Hence, there may be risks that developers do not need to consider when using specific languages such as Solidity of Ethereum. Furthermore, even though development tools exist, how many risks are covered by the tools is an open question. In this paper, we focus on Go language and the tools. First, we surveyed what kind of risks are associated with chaincodes are developed using Go language and observed there are 14 potential risks. Then, we investigated how many risks can be covered by Go tools, e.g., golint and gosec, and a vulnerability detection tool for chaincodes called Chaincode Scanner. From our results, we observed that some risks are not covered by the existing tools. Hence, we develop a detection tool to cover risks by static analysis. Finally, in this paper, we describe how to find the risks with our tool and evaluate the usefulness.
Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization

Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models generate unfocused summaries.We develop an abstractive meeting summarizer from both videos and audios of meeting recordings.Specifically, we propose a multi-modal hierarchical attention mechanism across three levels: topic segment, utterance and word.To narrow down the focus into topically-relevant segments, we jointly model topic segmentation and summarization.In addition to traditional textual features, we introduce new multi-modal features derived from visual focus of attention, based on the assumption that an utterance is more important if its speaker receives more attention.Experiments show that our model significantly outperforms the state-of-the-art with both BLEU and ROUGE measures.