Reviewerly/exHarmony huggingface dataset:
Temporal Planning with Problems Requiring Concurrency through Action Graphs and Local Search

We present an extension of the planning framework based on action graphs and local search to deal with PDDL2.1 temporal problems requiring concurrency, while previously the approach could only solve problems admitting a sequential solution. The paper introduces a revised plan representation supporting concurrency and some new search techniques using it, which are implemented in a new version of the LPG planner. An experimental analysis indicates that the proposed approach is suitable to temporal planning with requiring concurrency and is competitive with state-of-the-art planners.
The Long-Term Bivariate Survival FGM Copula Model: An Application to a Brazilian HIV Data

In this paper we propose a new bivariate long-term distribution based on the Farlie-Gumbel-Morgenstern copula model.The proposed model allows for the presence of censored data and covariates in the cure parameter.For inferential purpose a Bayesian approach via Markov Chain Monte Carlo (MCMC) is considered.Further, some discussions on the model selection criteria are given.In order to examine outlying and influential observations, we develop a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence.The newly developed procedures are illustrated on artificial and real HIV data.
Real-Time Underwater Image Recognition with FPGA Embedded System for Convolutional Neural Network

The underwater environment is still unknown for humans, so the high definition camera is an important tool for data acquisition at short distances underwater. Due to insufficient power, the image data collected by underwater submersible devices cannot be analyzed in real time. Based on the characteristics of Field-Programmable Gate Array (FPGA), low power consumption, strong computing capability, and high flexibility, we design an embedded FPGA image recognition system on Convolutional Neural Network (CNN). By using two technologies of FPGA, parallelism and pipeline, the parallelization of multi-depth convolution operations is realized. In the experimental phase, we collect and segment the images from underwater video recorded by the submersible. Next, we join the tags with the images to build the training set. The test results show that the proposed FPGA system achieves the same accuracy as the workstation, and we get a frame rate at 25 FPS with the resolution of 1920 × 1080. This meets our needs for underwater identification tasks.
NightOwls: A Pedestrians at Night Dataset

We introduce a comprehensive public dataset, NightOwls, for pedestrian detection at night. In comparison to daytime conditions, pedestrian detection at night is more challenging due to variable and low illumination, reflections, blur, and changing contrast. NightOwls consists of 279k frames in 40 sequences recorded at night across 3 countries by an industry-standard camera, including different seasons and weather conditions. All the frames are fully annotated and contain additional object attributes such as occlusion, pose and difficulty, as well as tracking information to identify the same object across multiple frames. A large number of background frames for evaluating the robustness of detectors is included, a validation set for local hyper-parameter tuning, as well as a testing set for central evaluation on a submission server is provided. As a baseline for pedestrian detection at night time, we compare the performance of ACF, Checkerboards, Faster R-CNN, RPN+BF, and SDS-RCNN. In particular, we demonstrate that state-of-the-art pedestrian detectors do not perform well at night, even when specifically trained on night data, and we show there is a clear gap in accuracy between day and night detections. We believe that the availability of a comprehensive night dataset may further advance the research of pedestrian detection, as well as object detection and tracking at night in general.
Review of short-text classification

Purpose Rapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type of document based on their content has a significant implication in many applications. The need to classify these documents in relevant classes according to their text contents should be interested in many practical reasons. Short-text classification is an essential step in many applications, such as spam filtering, sentiment analysis, Twitter personalization, customer review and many other applications related to social networks. Reviews on short text and its application are limited. Thus, this paper aims to discuss the characteristics of short text, its challenges and difficulties in classification. The paper attempt to introduce all stages in principle classification, the technique used in each stage and the possible development trend in each stage. Design/methodology/approach The paper as a review of the main aspect of short-text classification. The paper is structured based on the classification task stage. Findings This paper discusses related issues and approaches to these problems. Further research could be conducted to address the challenges in short texts and avoid poor accuracy in classification. Problems in low performance can be solved by using optimized solutions, such as genetic algorithms that are powerful in enhancing the quality of selected features. Soft computing solution has a fuzzy logic that makes short-text problems a promising area of research. Originality/value Using a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. Current solutions still have low performance, implying the need for improvement. This paper discusses related issues and approaches to these problems.
Fall prediction using behavioural modelling from sensor data in smart homes

The number of methods for identifying potential fall risk is growing as the rate of elderly fallers continues to rise in the UK. Assessments for identifying risk of falling are usually performed in hospitals and other laboratory environments, however these are costly and cause inconvenience for the subject and health services. Replacing these intrusive testing methods with a passive in-home monitoring solution would provide a less time-consuming and cheaper alternative. As sensors become more readily available, machine learning models can be applied to the large amount of data they produce. This can support activity recognition, falls detection, prediction and risk determination. In this review, the growing complexity of sensor data, the required analysis, and the machine learning techniques used to determine risk of falling are explored. The current research on using passive monitoring in the home is discussed, while the viability of active monitoring using vision-based and wearable sensors is considered. Methods of fall detection, prediction and risk determination are then compared.
On Connected Sublevel Sets in Deep Learning

This paper shows that every sublevel set of the loss function of a class of deep over-parameterized neural nets with piecewise linear activation functions is connected and unbounded. This implies that the loss has no bad local valleys and all of its global minima are connected within a unique and potentially very large global valley.
Unsupervised learning of object structure and dynamics from videos

Extracting and predicting object structure and dynamics from videos without supervision is a major challenge in machine learning. To address this challenge, we adopt a keypoint-based image representation and learn a stochastic dynamics model of the keypoints. Future frames are reconstructed from the keypoints and a reference frame. By modeling dynamics in the keypoint coordinate space, we achieve stable learning and avoid compounding of errors in pixel space. Our method improves upon unstructured representations both for pixel-level video prediction and for downstream tasks requiring object-level understanding of motion dynamics. We evaluate our model on diverse datasets: a multi-agent sports dataset, the Human3.6M dataset, and datasets based on continuous control tasks from the DeepMind Control Suite. The spatially structured representation outperforms unstructured representations on a range of motion-related tasks such as object tracking, action recognition and reward prediction.
Channel Gating Neural Networks

This paper introduces channel gating, a dynamic, fine-grained, and hardware－efficient pruning scheme to reduce the computation cost for convolutional neural networks (CNNs). Channel gating identifies regions in the features that contribute less to the classification result, and skips the computation on a subset of the input channels for these ineffective regions. Unlike static network pruning, channel gating optimizes CNN inference at run-time by exploiting input-specific characteristics, which allows substantially reducing the compute cost with almost no accuracy loss. We experimentally show that applying channel gating in state-of-the-art networks achieves 2.7-8.0x reduction in floating-point operations (FLOPs) and 2.0-4.4x reduction in off-chip memory accesses with a minimal accuracy loss on CIFAR-10. Combining our method with knowledge distillation reduces the compute cost of ResNet-18 by 2.6x without accuracy drop on ImageNet. We further demonstrate that channel gating can be realized in hardware efficiently. Our approach exhibits sparsity patterns that are well-suited to dense systolic arrays with minimal additional hardware. We have designed an accelerator for channel gating networks, which can be implemented using either FPGAs or ASICs. Running a quantized ResNet-18 model for ImageNet, our accelerator achieves an encouraging speedup of 2.4x on average, with a theoretical FLOP reduction of 2.8x.
A Unified Multiple Graph Learning and Convolutional Network Model for Co-saliency Estimation

Co-saliency estimation which aims to identify the common salient object regions contained in an image set is an active problem in computer vision. The main challenge for co-saliency estimation problem is how to exploit the salient cues of both intra-image and inter-image simultaneously. In this paper, we first represent intra-image and inter-image as intra-graph and inter-graph respectively and formulate co-saliency estimation as graph nodes labeling. Then, we propose a novel multiple graph learning and convolutional network (M-GLCN) for image co-saliency estimation. M-GLCN conducts graph convolutional learning and labeling on both inter-graph and intra-graph cooperatively and thus can well exploit the salient cues of both intra-image and inter-image simultaneously for co-saliency estimation. Moreover, M-GLCN employs a new graph learning mechanism to learn both inter-graph and intra-graph adaptively. Experimental results on several benchmark datasets demonstrate the effectiveness of M-GLCN on co-saliency estimation task.
Improving Entity Linking by Modeling Latent Entity Type Information

Existing state of the art neural entity linking models employ attention-based bag-of-words context model and pre-trained entity embeddings bootstrapped from word embeddings to assess topic level context compatibility. However, the latent entity type information in the immediate context of the mention is neglected, which causes the models often link mentions to incorrect entities with incorrect type. To tackle this problem, we propose to inject latent entity type information into the entity embeddings based on pre-trained BERT. In addition, we integrate a BERT-based entity similarity score into the local context model of a state-of-the-art model to better capture latent entity type information. Our model significantly outperforms the state-of-the-art entity linking models on standard benchmark (AIDA-CoNLL). Detailed experiment analysis demonstrates that our model corrects most of the type errors produced by the direct baseline.
Blockchain-based agri-food supply chain management: case study in China

The fundamental purpose of agri-food supply chain management is to restrict opportunism caused by information asymmetry. Traditional Chinese agri-food supply chain management introduces a contract mechanism and a trust mechanism to manage the uncertainty of the agri-food quasi-organization. However, it is almost impossible to improve the efficiency of transactions and maintain agri-food supply chain stability in the case of asymmetric information. Nowadays, blockchain, Internet of Things technology and big data drive the agri-food supply chain into a vast smart network which would break the information constraints. This paper analyzes the coupling between blockchain-based digital system and the agri-food supply chain. In addition, this paper presents two cases from China, indicating that the proposed blockchain-based system can achieve disruptive transformation in agri-food supply chain management.
Energy Efficient Virtual Machines Placement Over Cloud-Fog Network Architecture

Fog computing is an emerging paradigm that aims to improve the efficiency and QoS of cloud computing by extending the cloud to the edge of the network. This paper develops a comprehensive energy efficiency analysis framework based on mathematical modeling and heuristics to study the offloading of virtual machine (VM) services from the cloud to the fog. The analysis addresses the impact of different factors including the traffic between the VM and its users, the VM workload, the workload versus number of users profile and the proximity of fog nodes to users. Overall, the power consumption can be reduced if the VM users' traffic is high and/or the VMs have a linear power profile. In such a linear profile case, the creation of multiple VM replicas does not increase the computing power consumption significantly (there may be a slight increase due to idle / baseline power consumption) if the number of users remains constant, however the VM replicas can be brought closer to the end users, thus reducing the transport network power consumption. In our scenario, the optimum placement of VMs over a cloud-fog architecture significantly decreased the total power consumption by 56% and 64% under high user data rates compared to optimized distributed clouds placement and placement in the existing AT&T network cloud locations, respectively.
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization

We propose a new stochastic first-order algorithmic framework to solve stochastic composite nonconvex optimization problems that covers both finite-sum and expectation settings. Our algorithms rely on the SARAH estimator introduced in (Nguyen et al, 2017) and consist of two steps: a proximal gradient and an averaging step making them different from existing nonconvex proximal-type algorithms. The algorithms only require an average smoothness assumption of the nonconvex objective term and additional bounded variance assumption if applied to expectation problems. They work with both constant and adaptive step-sizes, while allowing single sample and mini-batches. In all these cases, we prove that our algorithms can achieve the best-known complexity bounds. One key step of our methods is new constant and adaptive step-sizes that help to achieve desired complexity bounds while improving practical performance. Our constant step-size is much larger than existing methods including proximal SVRG schemes in the single sample case. We also specify the algorithm to the non-composite case that covers existing state-of-the-arts in terms of complexity bounds. Our update also allows one to trade-off between step-sizes and mini-batch sizes to improve performance. We test the proposed algorithms on two composite nonconvex problems and neural networks using several well-known datasets.
The asymmetric effect of bitcoin on altcoins: evidence from the nonlinear autoregressive distributed lag (NARDL) model

This paper examines the asymmetric effect of Bitcoin on three altcoins, namely Ethereum (ETH), Ripple (XRP) and Litecoin (LTC) by using the Nonlinear Autoregressive Distributed Lag (NARDL) model for the period July 2015 to March 2019. We provide evidence on the asymmetric impact of Bitcoin on altcoins both in the short-run and in the long-run. In the short-run, a decrease in Bitcoin price has greater effect than an increase on the prices of altcoins. This asymmetry is more frequent after the 2017 cryptocurrency price crash.
Joint Speaker Counting, Speech Recognition, and Speaker Identification for Overlapped Speech of any Number of Speakers

We propose an end-to-end speaker-attributed automatic speech recognition model that unifies speaker counting, speech recognition, and speaker identification on monaural overlapped speech. Our model is built on serialized output training (SOT) with attention-based encoder-decoder, a recently proposed method for recognizing overlapped speech comprising an arbitrary number of speakers. We extend SOT by introducing a speaker inventory as an auxiliary input to produce speaker labels as well as multi-speaker transcriptions. All model parameters are optimized by speaker-attributed maximum mutual information criterion, which represents a joint probability for overlapped speech recognition and speaker identification. Experiments on LibriSpeech corpus show that our proposed method achieves significantly better speaker-attributed word error rate than the baseline that separately performs overlapped speech recognition and speaker identification.
An Analysis of Natural Language Inference Benchmarks through the Lens of Negation

Negation is underrepresented in existing natural language inference benchmarks.Additionally, one can often ignore the few negations in existing benchmarks and still make the right inference judgments.In this paper, we present a new benchmark for natural language inference in which negation plays an important role.We also show that state-of-the-art transformers struggle making inference judgments with the new pairs. Original pairNew pair w/ negation RTE T: Tropical Storm Debby is blamed for several deaths across the Caribbean.T neg : Tropical Storm Debby is not blamed for several
MOPO: Model-based Offline Policy Optimization

Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a large batch of previously collected data. This problem setting offers the promise of utilizing such datasets to acquire policies without any costly or dangerous active exploration. However, it is also challenging, due to the distributional shift between the offline training data and those states visited by the learned policy. Despite significant recent progress, the most successful prior methods are model-free and constrain the policy to the support of data, precluding generalization to unseen states. In this paper, we first observe that an existing model-based RL algorithm already produces significant gains in the offline setting compared to model-free approaches. However, standard model-based RL methods, designed for the online setting, do not provide an explicit mechanism to avoid the offline setting's distributional shift issue. Instead, we propose to modify the existing model-based RL methods by applying them with rewards artificially penalized by the uncertainty of the dynamics. We theoretically show that the algorithm maximizes a lower bound of the policy's return under the true MDP. We also characterize the trade-off between the gain and risk of leaving the support of the batch data. Our algorithm, Model-based Offline Policy Optimization (MOPO), outperforms standard model-based RL algorithms and prior state-of-the-art model-free offline RL algorithms on existing offline RL benchmarks and two challenging continuous control tasks that require generalizing from data collected for a different task. The code is available at this https URL.
An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels

Large-scale Multi-label Text Classification (LMTC) has a wide range of Natural Language Processing (NLP) applications and presents interesting challenges.First, not all labels are well represented in the training set, due to the very large label set and the skewed label distributions of LMTC datasets.Also, label hierarchies and differences in human labelling guidelines may affect graph-aware annotation proximity.Finally, the label hierarchies are periodically updated, requiring LMTC models capable of zero-shot generalization.Current state-of-the-art LMTC models employ Label-Wise Attention Networks (LWANs), which (1) typically treat LMTC as flat multi-label classification; (2) may use the label hierarchy to improve zero-shot learning, although this practice is vastly understudied; and (3) have not been combined with pre-trained Transformers (e.g.BERT), which have led to state-of-the-art results in several NLP benchmarks.Here, for the first time, we empirically evaluate a battery of LMTC methods from vanilla LWANs to hierarchical classification approaches and transfer learning, on frequent, few, and zero-shot learning on three datasets from different domains.We show that hierarchical methods based on Probabilistic Label Trees (PLTs) outperform LWANs.Furthermore, we show that Transformer-based approaches outperform the state-of-the-art in two of the datasets, and we propose a new state-of-the-art method which combines BERT with LWAN.Finally, we propose new models that leverage the label hierarchy to improve few and zero-shot learning, considering on each dataset a graph-aware annotation proximity measure that we introduce.
Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

Due to the constantly increasing demand for automatic tracking and recognition systems, there is a need for more proficient, intelligent and sustainable human activity tracking. The main purpose of this study is to develop an accurate and sustainable human action tracking system that is capable of error-free identification of human movements irrespective of the environment in which those actions are performed. Therefore, in this paper we propose a stereoscopic Human Action Recognition (HAR) system based on the fusion of RGB (red, green, blue) and depth sensors. These sensors give an extra depth of information which enables the three-dimensional (3D) tracking of each and every movement performed by humans. Human actions are tracked according to four features, namely, (1) geodesic distance; (2) 3D Cartesian-plane features; (3) joints Motion Capture (MOCAP) features and (4) way-points trajectory generation. In order to represent these features in an optimized form, Particle Swarm Optimization (PSO) is applied. After optimization, a neuro-fuzzy classifier is used for classification and recognition. Extensive experimentation is performed on three challenging datasets: A Nanyang Technological University (NTU) RGB+D dataset; a UoL (University of Lincoln) 3D social activity dataset and a Collective Activity Dataset (CAD). Evaluation experiments on the proposed system proved that a fusion of vision sensors along with our unique features is an efficient approach towards developing a robust HAR system, having achieved a mean accuracy of 93.5% with the NTU RGB+D dataset, 92.2% with the UoL dataset and 89.6% with the Collective Activity dataset. The developed system can play a significant role in many computer vision-based applications, such as intelligent homes, offices and hospitals, and surveillance systems.