Reviewerly/exHarmony huggingface dataset:
End-to-End Deep Reinforcement Learning based Recommendation with Supervised Embedding

The research of reinforcement learning (RL) based recommendation method has become a hot topic in recommendation community, due to the recent advance in interactive recommender systems. The existing RL recommendation approaches can be summarized into a unified framework with three components, namely embedding component (EC), state representation component (SRC) and policy component (PC). We find that EC cannot be nicely trained with the other two components simultaneously. Previous studies bypass the obstacle through a pre-training and fixing strategy, which makes their approaches unlike a real end-to-end fashion. More importantly, such pre-trained and fixed EC suffers from two inherent drawbacks: (1) Pre-trained and fixed embeddings are unable to model evolving preference of users and item correlations in the dynamic environment; (2) Pre-training is inconvenient in the industrial applications. To address the problem, in this paper, we propose an End-to-end Deep Reinforcement learning based Recommendation framework (EDRR). In this framework, a supervised learning signal is carefully designed for smoothing the update gradients to EC, and three incorporating ways are introduced and compared. To the best of our knowledge, we are the first to address the training compatibility between the three components in RL based recommendations. Extensive experiments are conducted on three real-world datasets, and the results demonstrate the proposed EDRR effectively achieves the end-to-end training purpose for both policy-based and value-based RL models, and delivers better performance than state-of-the-art methods.
Rule-Guided Compositional Representation Learning on Knowledge Graphs

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Moreover, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.
On Sharding Permissioned Blockchains

Permissioned Blockchain systems rely mainly on Byzantine fault-tolerant protocols to establish consensus on the order of transactions. While Byzantine fault-tolerant protocols mostly guarantee consistency (safety) in an asynchronous network using 3f+1 machines to overcome the simultaneous malicious failure of any f nodes, in many systems, e.g., blockchain systems, the number of available nodes (resources) is much more than 3f + 1. To utilize such extra resources, in this paper we introduce a model that leverages transaction parallelism by partitioning the nodes into clusters (partitions) and processing independent transactions on different partitions simultaneously. The model also shards the blockchain ledger, assigns different shards of the blockchain ledger to different clusters, and includes both intra-shard and cross-shard transactions. Since more than one cluster is involved in each cross-shard transaction, the ledger is formed as a directed acyclic graph.
Opinions on Sustainability of Smart Cities in the Context of Energy Challenges Posed by Cryptocurrency Mining

Next to climate change on the list of challenges faced by humankind in today’s technological age is energy management. While “smart” ideas continue to gather momentum as some of the ways earmarked to combat the menace of a changing climate, coupled with efficient management of energy, research and development in the blockchain is not retracting, recently giving rise to digital currencies capable of fueling massive energy consumption via mining of “crypto-coins”. Given that sustainability is a crucial goal in the design of smart cities nowadays, there are currently no assurances of sustainable cities where cryptocurrency mining is at full scale. Nevertheless, alternative energy sources may come to the rescue in no distant time. In this paper, we contextualize energy-use in smart cities through mining of virtual currencies, in order to predict whether or not smart cities can truly be sustainable if crypto-mining is sustained. An attempt is also made to emphasize the possible ways of reducing energy use and all activities involving digital currencies by seeking to replace “Proof of Work” (PoW) with improved alternatives.
Event-Driven Video Frame Synthesis

Temporal Video Frame Synthesis (TVFS) aims at synthesizing novel frames at timestamps different from existing frames, which has wide applications in video codec, editing and analysis. In this paper, we propose a high frame-rate TVFS framework which takes hybrid input data from a low-speed frame-based sensor and a high-speed event-based sensor. Compared to frame-based sensors, event-based sensors report brightness changes at very high speed, which may well provide useful spatio-temoral information for high frame-rate TVFS. Therefore, we first introduce a differentiable fusion model to approximate the dual-modal physical sensing process, unifying a variety of TVFS scenarios, e.g., interpolation, prediction and motion deblur. Our differentiable model enables iterative optimization of the latent video tensor via autodifferentiation, which propagates the gradients of a loss function defined on the measured data. Our differentiable model-based reconstruction does not involve training, yet is parallelizable and can be implemented on machine learning platforms (such as TensorFlow). Second, we develop a deep learning strategy to enhance the results from the first step, which we refer as a residual "denoising" process. Our trained "denoiser" is beyond Gaussian denoising and shows properties such as contrast enhancement and motion awareness. We show that our framework is capable of handling challenging scenes including both fast motion and strong occlusions.
Practical parallel hypergraph algorithms

While there has been significant work on parallel graph processing, there has been very surprisingly little work on high-performance hypergraph processing. This paper presents a collection of efficient parallel algorithms for hypergraph processing, including algorithms for betweenness centrality, maximal independent set, k-core decomposition, hypertrees, hyperpaths, connected components, PageRank, and single-source shortest paths. For these problems, we either provide new parallel algorithms or more efficient implementations than prior work. Furthermore, our algorithms are theoretically-efficient in terms of work and depth. To implement our algorithms, we extend the Ligra graph processing framework to support hypergraphs, and our implementations benefit from graph optimizations including switching between sparse and dense traversals based on the frontier size, edge-aware parallelization, using buckets to prioritize processing of vertices, and compression. Our experiments on a 72-core machine and show that our algorithms obtain excellent parallel speedups, and are significantly faster than algorithms in existing hypergraph processing frameworks.
A new pyramidal opponent color-shape model based video shot boundary detection

Video shot boundary detection (VSBD) is one of the most essential criteria for many intelligent video analysis-related applications, such as video retrieval, indexing, browsing, categorization and summarization. VSBD aims to segment big video data into meaningful fragments known as shots. This paper put forwards a new pyramidal opponent colour-shape (POCS) model which can detect abrupt transition (AT) and gradual transition (GT) simultaneously, even in the presence of illumination changes, huge object movement between frames, and fast camera motion. First, the content of frames in the video subjected to VSBD is represented by the proposed POCS model. Consequently, the temporal nature of the POCS model is subjected to a suitable segment (SS) selection procedure in order to minimize the complexity of VSBD method. The SS from the video frames is examined for transitions within it using a bagged-trees classifier (BTC) learned on a balanced training set via parallel processing. To prove the superiority of the proposed VSBD algorithm, it is evaluated on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets for classifying the basic units of video according to no transition (NT), AT and GT. The experimental evaluation results in an F1-score of 95.13%, 98.13% and 97.11% on the TRECVID 2001, TRECVID2007 and VIDEOSEG2004 data sets, respectively.
An association-constrained LDA model for joint extraction of product aspects and opinions

The Latent Dirichlet Allocation (LDA) model, which is a document-level probabilistic model, has been widely used in topic modeling. However, an essential issue of the LDA is its shortage in identifying co-occurrence relationships (e.g., aspect-aspect, aspect-opinion, etc.) in sentences. To address the problem, we propose an association constrained LDA (AC-LDA) for effectively capturing the co-occurrence relationships. Specifically, based on the basic features of the syntactic structure in product reviews, we formalize three major types of word association combinations and then carefully design corresponding identifications. For reducing the influence of global aspect words on the local distribution, we apply an important constraint on global aspects. Finally, the constraint and related association combinations are merged into the LDA to guide the topic-words allocation in the learning process. Based on the experiments on real-world product review data, we demonstrate that our model can effectively capture the relationships hidden in local sentences and further increase the extraction rate of fine-grained aspects and opinion words. Our results confirm the superiority of the AC-LDA over the state-of-the-art methods in terms of the extraction accuracy. We also verify the strength of our method in identifying irregularly appeared terms, such as non-aspect opinions, low-frequency words, and secondary aspects.
Chaos-based Vortex Search algorithm for solving inverse kinematics problem of serial robot manipulators with offset wrist

Vortex Search (VS) algorithm is a single-solution-based optimization algorithm that requires the high maximum number of iterations (NOI) to solve optimization problems. In this study, two methods were proposed to reduce the required maximum NOI of the VS algorithm. These methods are based on using ten chaos maps with the VS algorithm and provide improvements in the exploration and exploitation abilities of the algorithm for reducing the required maximum NOI. Ten chaos-based VS algorithms (CVSs) were obtained by combining these methods with the VS algorithm. The performances of the CVS algorithms were tested by fifty benchmark functions. The results were evaluated in terms of some statistical values and a pairwise statistical test, Wilcoxon Signed-Rank Test. According to the results, it was found that the CVS algorithm obtained by using the Gauss–Mouse chaos map was the best algorithm. And also, it was shown that the proposed CVS algorithm performs better than the classical VS algorithm, even when its maximum NOI was ten times less than the maximum NOI of the VS algorithm. Additionally, the effects of the proposed methods in the exploration and the exploitation abilities of the VS algorithm were visually shown and a comparison about algorithm processing time was presented. In order to test the performance of the proposed CVS algorithm in solving the real-world optimization problems, the inverse kinematics problem of a six Degrees Of Freedom (DOF) serial robot manipulator with offset wrist was solved with both the proposed CVS algorithm and the VS algorithm for two different types of trajectories. The results showed that the proposed algorithm outperforms the VS algorithm in terms of the objective function values and position errors of the end-effector of the serial robot manipulator.
Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter

Object tracking has long been an active research topic in image processing and computer vision fields with various application areas. For practical applications, the object tracking technique should be not only accurate but also fast in a real-time streaming condition. Recently, deep feature-based trackers have been proposed to achieve a higher accuracy, but those are not suitable for real-time tracking because of an extremely slow processing speed. The slow speed is a major factor to degrade tracking accuracy under a real-time streaming condition since the processing delay forces skipping frames. To increase the tracking accuracy with preserving the processing speed, this paper presents an improved kernelized correlation filter (KCF)-based tracking method that integrates three functional modules: (i) tracking failure detection, (ii) re-tracking using multiple search windows, and (iii) motion vector analysis to decide a preferred search window. Under a real-time streaming condition, the proposed method yields better results than the original KCF in the sense of tracking accuracy, and when a target has a very large movement, the proposed method outperforms a deep learning-based tracker, such as multi-domain convolutional neural network (MDNet).
Practical traceable multi-authority CP-ABE with outsourcing decryption and access policy updation

Multi-authority ciphertext policy attribute-based encryption (CP-ABE) is one of the promising approaches where multiple authorities issue and manage various attributes to achieve fine-grained access control on stored data in cloud platforms. However, the practical use of multi-authority CP-ABE (MCP-ABE) requires satisfaction of different features. Firstly, in MCP-ABE, users having the same set of attributes must share the same decryption privileges. This may potentially lead to exposure of decryption keys by some malicious users. Thus, there is a need of designing MCP-ABE with traceability to overcome this problem. Secondly, as the cost of decryption increases with the complexity of access policy, it is preferred to provide the functionality of outsourcing decryption to reduce the computational burden on the user. Finally, the data owners must have flexibility to change the access policy. Hence, policy updation must be an integral part of MCP-ABE scheme. In this paper, we construct a new multi-authority CP-ABE scheme over large attribute universe with decentralization of authorities that supports white-box traceability along with policy updation and outsourcing decryption. Our proposed cryptosystem is built on prime order groups and supports monotonic access structures. These features improve efficiency and expressiveness of our system. We implemented our proposed cryptosystem in Charm cryptographic framework and analyzed its performance with a diverse set of test cases.
Hand gesture recognition using multimodal data fusion and multiscale parallel convolutional neural network for human–robot interaction

Abstract Hand gesture recognition plays an important role in human–robot interaction. The accuracy and reliability of hand gesture recognition are the keys to gesture‐based human–robot interaction tasks. To solve this problem, a method based on multimodal data fusion and multiscale parallel convolutional neural network (CNN) is proposed in this paper to improve the accuracy and reliability of hand gesture recognition. First of all, data fusion is conducted on the sEMG signal, the RGB image, and the depth image of hand gestures. Then, the fused images are generated to two different scale images by downsampling, which are respectively input into two subnetworks of the parallel CNN to obtain two hand gesture recognition results. After that, hand gesture recognition results of the parallel CNN are combined to obtain the final hand gesture recognition result. Finally, experiments are carried out on a self‐made database containing 10 common hand gestures, which verify the effectiveness and superiority of the proposed method for hand gesture recognition. In addition, the proposed method is applied to a seven‐degree‐of‐freedom bionic manipulator to achieve robotic manipulation with hand gestures.
Model-Free Distortion Rectification Framework Bridged by Distortion Distribution Map

Recently, learning-based distortion rectification schemes have shown high efficiency. However, most of these methods only focus on a specific camera model with fixed parameters, thus failing to be extended to other models. To avoid such a disadvantage, we propose a model-free distortion rectification framework for the single-shot case, bridged by the distortion distribution map (DDM). Our framework is based on an observation that the pixel-wise distortion information is explicitly regular in a distorted image, despite different models having different types and numbers of distortion parameters. Motivated by this observation, instead of estimating the heterogeneous distortion parameters, we construct a proposed distortion distribution map that intuitively indicates the global distortion features of a distorted image. In addition, we develop a dual-stream feature learning module, benefitting from both the advantages of traditional methods that leverage the local handcrafted feature and learning-based methods that focus on the global semantic feature perception. Due to the sparsity of handcrafted features, we discrete the features into a 2D point map and learn the structure inspired by PointNet. Finally, a multimodal attention fusion module is designed to attentively fuse the local structural and global semantic features, providing the hybrid features for the more reasonable scene recovery. The experimental results demonstrate the excellent generalization ability and more significant performance of our method in both quantitative and qualitative evaluations, compared with the state-of-the-art methods.
Modeling relative permeability of gas condensate reservoirs: Advanced computational frameworks

In the last years, an appreciable effort has been directed toward developing empirical models to link the relative permeability of gas condensate reservoirs to the interfacial tension and velocity as well as saturation. However, these models suffer from non-universality and uncertainties in setting the tuning parameters. In order to alleviate the aforesaid infirmities in this study, comprehensive modeling was carried out by employing numerous smart computer-aided algorithms including Support Vector Regression (SVR), Least Square Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), Multilayer Perceptron (MLP), Group Method of Data Handling (GMDH), and Gene Expression Programming (GEP) as predictors and Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Levenberg-Marquardt Algorithm (LMA), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG) and Randomized Polynomial Time (RP) as optimizers. To this end, a wide variety of reliable databanks encompasses more than 1000 data points from eights sets of experimental data was utilized in the training and testing steps of the modeling process. The predictors were integrated with optimization algorithms to assign the optimum tuning parameters of each model. The modeling was implemented in two different manners from the standpoint of models inputs: (1) 2-input (saturation and capillary number); (2) 3-input (saturation, interfacial tension, and capillary number). The results of the comparison between these strategies demonstrate more accuracy of the models when employing three independent parameters as the input (3-input). Among the developed models, the MLP-LMA modeling algorithm outperformed all other models with root mean square errors (RMSEs) of 0.035 and 0.019 for gas and condensate phases, respectively. At the end, in a comparison between both 2-input and 3-input MLP-LMA models and five traditional literature models, both smart modeling approaches were established themselves as the most accurate techniques for estimation of relative permeability in gas condensate reservoirs.
A regularized convolutional neural network for semantic image segmentation

Convolutional neural networks (CNNs) have achieved prominent performance in a series of image processing problems. CNNs become the first choice for dense classification problems such as semantic segmentation. However, CNNs predict the class of each pixel independently in semantic segmentation tasks, spatial regularity of the segmented objects is still a problem for these methods. Especially when given few training data, CNN could not perform well in the details, isolated and scattered small regions often appear in all kinds of CNN segmentation results. In this paper, we propose a method to add spatial regularization to the segmented objects. In our method, the spatial regularization such as total variation (TV) can be easily integrated into CNN network and it produces smooth edges and eliminate isolated points. We apply our proposed method to Unet and Segnet, which are well-established CNNs for image segmentation, and test them on WBC and CamVid datasets, respectively. The results show that the details of predictions are well improved by regularized networks.