Reviewerly/exHarmony huggingface dataset:
Better Rewards Yield Better Summaries: Learning to Summarise Without References

Reinforcement Learning (RL) based document summarisation systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during training.However, summaries with high ROUGE scores often receive low human judgement.To find a better reward function that can guide RL to generate human-appealing summaries, we learn a reward function from human ratings on 2,500 summaries.Our reward function only takes the document and system summary as input.Hence, once trained, it can be used to train RL-based summarisation systems without using any reference summaries.We show that our learned rewards have significantly higher correlation with human ratings than previous approaches.Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summaries with higher human ratings.The learned reward function and our source code are available at https:
A Privacy Protection Scheme of Microgrid Direct Electricity Transaction Based on Consortium Blockchain and Continuous Double Auction

Low cost, high efficiency, price transparency, and timely settlement of transactions are required for direct transactions between electricity providers and consumers in the microgrids.So the blockchain technology and the continuous double auction mechanism for direct electricity trading have always been a hot topic in the field of microgrids.In order to further reduce the transaction cost of blockchain and increase the transaction efficiency, and to solve the problem of lack of privacy protection for continuous double auction in the existing scheme, a privacy protection scheme of microgrids direct electricity transaction based on consortium blockchain and the continuous double auction is proposed.In it, the combination of consortium blockchain technology and continuous double auction mechanism is applied to reduce costs and improve the efficiency of transactions.In the meanwhile, pseudonyms and pseudonym certificates are generated by fair blind signature technology to realize identity privacy in the continuous double auction.And decentralization and user identity traceability are achieved by using (t, n) threshold secret sharing technology which distributes and recovers the private key of a trusted third party.The theoretical security analysis shows that the privacy protection scheme has higher security.The simulation experiment shows that the consortium blockchain technology has lower cost and higher efficiency in this scheme.
Communication Enhancement through Quantum Coherent Control of N Channels in an Indefinite Causal-Order Scenario

In quantum Shannon theory, transmission of information is enhanced by quantum features. Up to very recently, the trajectories of transmission remained fully classical. Recently, a new paradigm was proposed by playing quantum tricks on two completely depolarizing quantum channels i.e., using coherent control in space or time of the two quantum channels. We extend here this control to the transmission of information through a network of an arbitrary number N of channels with arbitrary individual capacity i.e., information preservation characteristics in the case of indefinite causal order. We propose a formalism to assess information transmission in the most general case of N channels in an indefinite causal order scenario yielding the output of such transmission. Then, we explicitly derive the quantum switch output and the associated Holevo limit of the information transmission for N = 2 , N = 3 as a function of all involved parameters. We find in the case N = 3 that the transmission of information for three channels is twice that of transmission of the two-channel case when a full superposition of all possible causal orders is used.
Attribute Propagation Network for Graph Zero-Shot Learning

The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that were not seen during training. To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes. In this paper, we aim to optimize the attribute space for ZSL by training a propagation mechanism to refine the semantic attributes of each class based on its neighbors and related classes on a graph of classes. We show that the propagated attributes can produce classifiers for zero-shot classes with significantly improved performance in different ZSL settings. The graph of classes is usually free or very cheap to acquire such as WordNet or ImageNet classes. When the graph is not provided, given pre-defined semantic embeddings of the classes, we can learn a mechanism to generate the graph in an end-to-end manner along with the propagation mechanism. However, this graph-aided technique has not been well-explored in the literature. In this paper, we introduce the “attribute propagation network (APNet)”, which is composed of 1) a graph propagation model generating attribute vector for each class and 2) a parameterized nearest neighbor (NN) classifier categorizing an image to the class with the nearest attribute vector to the image's embedding. For better generalization over unseen classes, different from previous methods, we adopt a meta-learning strategy to train the propagation mechanism and the similarity metric for the NN classifier on multiple sub-graphs, each associated with a classification task over a subset of training classes. In experiments with two zero-shot learning settings and five benchmark datasets, APNet achieves either compelling performance or new state-of-the-art results.
The Dynamics of Drift in Digitized Processes

This paper uses a simulation to build new theory about complexity and phase change in processes that are supported by digital technologies. We know that digitized processes can drift (change incrementally over time). We simulate this phenomenon by incre
Ultrafast Photorealistic Style Transfer via Neural Architecture Search

The key challenge in photorealistic style transfer is that an algorithm should faithfully transfer the style of a reference photo to a content photo while the generated image should look like one captured by a camera. Although several photorealistic style transfer algorithms have been proposed, they need to rely on post- and/or pre-processing to make the generated images look photorealistic. If we disable the additional processing, these algorithms would fail to produce plausible photorealistic stylization in terms of detail preservation and photorealism. In this work, we propose an effective solution to these issues. Our method consists of a construction step (C-step) to build a photorealistic stylization network and a pruning step (P-step) for acceleration. In the C-step, we propose a dense auto-encoder named PhotoNet based on a carefully designed pre-analysis. PhotoNet integrates a feature aggregation module (BFA) and instance normalized skip links (INSL). To generate faithful stylization, we introduce multiple style transfer modules in the decoder and INSLs. PhotoNet significantly outperforms existing algorithms in terms of both efficiency and effectiveness. In the P-step, we adopt a neural architecture search method to accelerate PhotoNet. We propose an automatic network pruning framework in the manner of teacher-student learning for photorealistic stylization. The network architecture named PhotoNAS resulted from the search achieves significant acceleration over PhotoNet while keeping the stylization effects almost intact. We conduct extensive experiments on both image and video transfer. The results show that our method can produce favorable results while achieving 20-30 times acceleration in comparison with the existing state-of-the-art approaches. It is worth noting that the proposed algorithm accomplishes better performance without any pre- or post-processing.
Training-Time-Friendly Network for Real-Time Object Detection

Modern object detectors can rarely achieve short training time, fast inference speed, and high accuracy at the same time. To strike a balance among them, we propose the Training-Time-Friendly Network (TTFNet). In this work, we start with light-head, single-stage, and anchor-free designs, which enable fast inference speed. Then, we focus on shortening training time. We notice that encoding more training samples from annotated boxes plays a similar role as increasing batch size, which helps enlarge the learning rate and accelerate the training process. To this end, we introduce a novel approach using Gaussian kernels to encode training samples. Besides, we design the initiative sample weights for better information utilization. Experiments on MS COCO show that our TTFNet has great advantages in balancing training time, inference speed, and accuracy. It has reduced training time by more than seven times compared to previous real-time detectors while maintaining state-of-the-art performances. In addition, our super-fast version of TTFNet-18 and TTFNet-53 can outperform SSD300 and YOLOv3 by less than one-tenth of their training time, respectively. The code has been made available at https://github.com/ZJULearning/ttfnet.
Occupancy-Map-Based Rate Distortion Optimization and Partition for Video-Based Point Cloud Compression

The video-based point cloud compression (V-PCC) is the state-of-the-art dynamic point cloud compression method developed by the Moving Pictures Experts Group (MPEG). It projects the point cloud patch by patch to its bounding box and organizes all the patches into a video to utilize the efficient video coding framework. However, the unoccupied pixels among different patches will lead to inefficiency of the video compression. First, the unoccupied pixels are treated equal to the occupied pixels in the rate distortion optimization process. However, the unoccupied pixels are useless for the reconstructed quality of the point cloud. Second, the edges of the occupied and unoccupied pixels can divide a coding unit into arbitrary shapes. Consequently, they are not well-characterized by the regular partitions supported in the video coding framework. Therefore, we propose using occupancy-map-based rate distortion optimization and partition to deal with these two problems. First, the occupancy map is used as a mask to ignore the distortions of the unoccupied pixels when calculating the rate distortion cost of a specified block. This strategy is applied to both intra prediction, inter prediction, and the sample adaptive offset to boost the performance. Second, we propose an occupancy-map-based partition to divide the occupied pixels from different patches and the unoccupied pixels into different prediction units (PUs). The motions of occupied PUs are then predicted using the auxiliary information. The unoccupied PU is finally padded using the approach that generates the geometry frames. The proposed algorithms are implemented in the V-PCC and High Efficiency Video Coding reference software. The experimental results show that the algorithms can individually contribute significant performance improvements compared with the V-PCC. Additionally, their combination can achieve even more bitrate savings than the sum of the individual algorithms.
Privacy Preserving Location Data Publishing: A Machine Learning Approach

Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to T-Drive, Geolife, and Gowalla location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.
A Cost Analysis of Internet of Things Sensor Data Storage on Blockchain via Smart Contracts

Blockchain is a developing technology that can be utilized for secure data storage and sharing. In this work, we examine the cost of Blockchain-based data storage for constrained Internet of Things (IoT) devices. We had two phases in the study. In the first phase, we stored data retrieved from a temperature/humidity sensor connected to an Ethereum testnet blockchain using smart contracts in two different ways: first, appending the new data to the existing data, storing all sensor data; and second, overwriting the new data onto the existing data, storing only a recent portion of the data. In the second phase, we stored simulated data from several sensors on the blockchain assuming sensor data is numeric. We proposed a method for encoding the data from the sensors in one variable and compared the costs of storing the data in an array versus storing the encoded data from all sensors in one variable. We also compared the costs of carrying out the encoding within the smart contract versus outside the smart contract. In the first phase, our results indicate that overwriting data points is more cost-efficient than appending them. In the second phase, using the proposed encoding method to store the data from several sensors costs significantly less than storing the data in an array, if the encoding is done outside the smart contract. If the encoding is carried out in the smart contract, the cost is still less than storing the data in an array, however, the difference is not significant. The study shows that even though expensive, for applications where the integrity and transparency of data are crucial, storing IoT sensor data on Ethereum could be a reliable solution.
Examining the Impact of Artificial Intelligence (AI)-Assisted Social Media Marketing on the Performance of Small and Medium Enterprises: Toward Effective Business Management in the Saudi Arabian Context

Purpose: To examine the impact of artificial intelligence-assisted social media marketing (AISMM)on the performance of startup businesses of small and medium enterprises (SMEs) in Saudi Arabia.Design/methodology/approach: A survey technique was employed whereby primary and secondary data was collected, analyzed, and interpreted.Participants involved business operators or employees of start-up businesses and SMEs in the Saudi Arabian context.Data were analyzed by using partial least squarestructure equation modeling (PLS-SEM).Findings: AISMM, which exhibits an increasing trend among start-up businesses and SMEs in Saudi Arabia, accounts for an overall increase in the number of customers and customer bases-and an additional tertiary effect of increased profitability.AISMM increases the effective business management and SMEs performance (SMEP).Moreover, effective business management increases the SMEP Originality/value: This study is quite unique as it is investigated that AISMM practices has significant role to enhance SMEP in which effective business management playing a mediating role.Implications: The practitioners can get help from this study to increase the performance by decreasing various problems of marketing by using AISMM.
Natural language processing for structuring clinical text data on depression using UK-CRIS

Background Utilisation of routinely collected electronic health records from secondary care offers unprecedented possibilities for medical science research but can also present difficulties. One key issue is that medical information is presented as free-form text and, therefore, requires time commitment from clinicians to manually extract salient information. Natural language processing (NLP) methods can be used to automatically extract clinically relevant information. Objective Our aim is to use natural language processing (NLP) to capture real-world data on individuals with depression from the Clinical Record Interactive Search (CRIS) clinical text to foster the use of electronic healthcare data in mental health research. Methods We used a combination of methods to extract salient information from electronic health records. First, clinical experts define the information of interest and subsequently build the training and testing corpora for statistical models. Second, we built and fine-tuned the statistical models using active learning procedures. Findings Results show a high degree of accuracy in the extraction of drug-related information. Contrastingly, a much lower degree of accuracy is demonstrated in relation to auxiliary variables. In combination with state-of-the-art active learning paradigms, the performance of the model increases considerably. Conclusions This study illustrates the feasibility of using the natural language processing models and proposes a research pipeline to be used for accurately extracting information from electronic health records. Clinical implications Real-world, individual patient data are an invaluable source of information, which can be used to better personalise treatment.
Metamorph: Injecting Inaudible Commands into Over-the-air Voice Controlled Systems

This paper presents Metamorph, a system that generates imperceptible audio that can survive over-the-air transmission to attack the neural network of a speech recognition system.The key challenge stems from how to ensure the added perturbation of the original audio in advance at the sender side is immune to unknown signal distortions during the transmission process.Our empirical study reveals that signal distortion is mainly due to device and channel frequency selectivity but with different characteristics.This brings a chance to capture and further pre-code this impact to generate adversarial examples that are robust to the over-the-air transmission.We leverage this opportunity in Metamorph and obtain an initial perturbation that captures the core distortion's impact from only a small set of prior measurements, and then take advantage of a domain adaptation algorithm to refine the perturbation to further improve the attack distance and reliability.Moreover, we consider also reducing human perceptibility of the added perturbation.Evaluation achieves a high attack success rate (90%) over the attack distance of up to 6 m.Within a moderate distance, e.g., 3 m, Metamorph maintains this high success rate, yet can be further adapted to largely improve the audio quality, confirmed by a human perceptibility study.
PM-LSH

Nearest neighbor (NN) search in high-dimensional spaces is inherently computationally expensive due to the curse of dimensionality. As a well-known solution to approximate NN search, locality-sensitive hashing (LSH) is able to answer c-approximate NN ( c -ANN) queries in sublinear time with constant probability. Existing LSH methods focus mainly on building hash bucket based indexing such that the candidate points can be retrieved quickly. However, existing coarse-grained structures fail to offer accurate distance estimation for candidate points, which translates into additional computational overhead when having to examine unnecessary points. This in turn reduces the performance of query processing. In contrast, we propose a fast and accurate LSH framework, called PM-LSH, that aims to compute the c -ANN query on large- scale, high-dimensional datasets. First, we adopt a simple yet effective PM-tree to index the data points. Second, we develop a tunable confidence interval to achieve accurate distance estimation and guarantee high result quality. Third, we propose an efficient algorithm on top of the PM-tree to improve the performance of computing c -ANN queries. Extensive experiments with real-world data offer evidence that PM-LSH is capable of outperforming existing proposals with respect to both efficiency and accuracy.
Render4Completion: Synthesizing Multi-View Depth Maps for 3D Shape Completion

We propose a novel approach for 3D shape completion by synthesizing multi-view depth maps. While previous work for shape completion relies on volumetric representations, meshes, or point clouds, we propose to use multi-view depth maps from a set of fixed viewing angles as our shape representation. This allows us to be free of the memory limitations of volumetric representations and point clouds by casting shape completion into an image-to-image translation problem. Specifically, we render depth maps of the incomplete shape from a fixed set of viewpoints, and perform depth map completion in each view. Different from image-to-image translation networks that process each view separately, our novel multi-view completion net (MVCN) leverages information from all views of a 3D shape to help the completion of each single view. This enables MVCN to leverage more information from different depth views to achieve high accuracy in single depth view completion, and improve the consistency among the completed depth images in different views. Benefiting from the multi-view representation and novel network structure, MVCN significantly improves the accuracy of 3D shape completion in large-scale benchmarks compared to the state of the art.
A parallel WOA with two communication strategies applied in DV-Hop localization method

Abstract Wireless sensor network (WSN) can effectively help us monitor the surrounding environment and prevent the occurrence of some natural disasters earlier, but we can only get the information of the surrounding environment correctly if we know the locations of nodes. How to know the exact positions of nodes is a strict challenge in WSN. Intelligent computing algorithms have been developed in recent years. They easily solve complex optimization problems, especially for those that cannot be modeled mathematically. This paper proposes a novel algorithm, named parallel whale optimization algorithm (PWOA). It contains two information exchange strategies between groups, and it significantly enhances global search ability and population diversity of the original whale optimization algorithm (WOA). Also, the algorithm is adopted to optimize the localization of WSN. Twenty-three mathematical optimization functions are accustomed to verifying the efficiency and effectiveness of the novel approach. Compared with some existing intelligent computing algorithms, the proposed PWOA may reach better results.