Reviewerly/exHarmony huggingface dataset:
Why Are Deep Representations Good Perceptual Quality Features?

Recently, intermediate feature maps of pre-trained convolutional neural networks have shown significant perceptual quality improvements, when they are used in the loss function for training new networks. It is believed that these features are better at encoding the perceptual quality and provide more efficient representations of input images compared to other perceptual metrics such as SSIM and PSNR. However, there have been no systematic studies to determine the underlying reason. Due to the lack of such an analysis, it is not possible to evaluate the performance of a particular set of features or to improve the perceptual quality even more by carefully selecting a subset of features from a pre-trained CNN. This work shows that the capabilities of pre-trained deep CNN features in optimizing the perceptual quality are correlated with their success in capturing basic human visual perception characteristics. In particular, we focus our analysis on fundamental aspects of human perception, such as the contrast sensitivity and orientation selectivity. We introduce two new formulations to measure the frequency and orientation selectivity of the features learned by convolutional layers for evaluating deep features learned by widely-used deep CNNs such as VGG-16. We demonstrate that the pre-trained CNN features which receive higher scores are better at predicting human quality judgment. Furthermore, we show the possibility of using our method to select deep features to form a new loss function, which improves the image reconstruction quality for the well-known single-image super-resolution problem.
Hierarchical Face Aging Through Disentangled Latent Characteristics

Current age datasets lie in a long-tailed distribution, which brings difficulties to describe the aging mechanism for the imbalance ages. To alleviate it, we design a novel facial age prior to guide the aging mechanism modeling. To explore the age effects on facial images, we propose a Disentangled Adversarial Autoencoder (DAAE) to disentangle the facial images into three independent factors: age, identity and extraneous information. To avoid the "wash away" of age and identity information in face aging process, we propose a hierarchical conditional generator by passing the disentangled identity and age embeddings to the high-level and low-level layers with class-conditional BatchNorm. Finally, a disentangled adversarial learning mechanism is introduced to boost the image quality for face aging. In this way, when manipulating the age distribution, DAAE can achieve face aging with arbitrary ages. Further, given an input face image, the mean value of the learned age posterior distribution can be treated as an age estimator. These indicate that DAAE can efficiently and accurately estimate the age distribution in a disentangling manner. DAAE is the first attempt to achieve facial age analysis tasks, including face aging with arbitrary ages, exemplar-based face aging and age estimation, in a universal framework. The qualitative and quantitative experiments demonstrate the superiority of DAAE on five popular datasets, including CACD2000, Morph, UTKFace, FG-NET and AgeDB.
Fine-Grained Mechanical Chinese Named Entity Recognition Based on ALBERT-AttBiLSTM-CRF and Transfer Learning

Manufacturing text often exists as unlabeled data; the entity is fine-grained and the extraction is difficult. The above problems mean that the manufacturing industry knowledge utilization rate is low. This paper proposes a novel Chinese fine-grained NER (named entity recognition) method based on symmetry lightweight deep multinetwork collaboration (ALBERT-AttBiLSTM-CRF) and model transfer considering active learning (MTAL) to research fine-grained named entity recognition of a few labeled Chinese textual data types. The method is divided into two stages. In the first stage, the ALBERT-AttBiLSTM-CRF was applied for verification in the CLUENER2020 dataset (Public dataset) to get a pretrained model; the experiments show that the model obtains an F1 score of 0.8962, which is better than the best baseline algorithm, an improvement of 9.2%. In the second stage, the pretrained model was transferred into the Manufacturing-NER dataset (our dataset), and we used the active learning strategy to optimize the model effect. The final F1 result of Manufacturing-NER was 0.8931 after the model transfer (it was higher than 0.8576 before the model transfer); so, this method represents an improvement of 3.55%. Our method effectively transfers the existing knowledge from public source data to scientific target data, solving the problem of named entity recognition with scarce labeled domain data, and proves its effectiveness.
Insights for AI from the human mind

How the cognitive sciences can inform the quest to build systems with the flexibility of the human mind.
A cross-domain recommender system through information transfer for medical diagnosis

The electronic diagnostic records of patients, primarily collected by hospitals, comprise valuable data for the development of recommender systems to support physicians in predicting the risks associated with various diseases. For some diseases, the diagnostic record data are not sufficient to train a prediction model to generate recommendations; this is referred to as the data sparsity problem. Cross-domain recommender systems offer a solution to this problem by transferring knowledge from a similar domain (source domain) with sufficient data for modeling to facilitate prediction in the current domain (target domain). However, building a cross-domain recommender system for medical diagnosis presents two challenges: (1) uncertain representations, such as the symptoms characterized by interval numbers, are often used in medical records, and (2) given two different diseases, the feature spaces of the two diagnostic domains are often disparate because the diseases are only likely to share a few symptoms. This study addresses these challenges by proposing a cross-domain recommender system, named information transfer for medical diagnosis (ITMD), to provide physicians with personalized recommendations for disease risks. In ITMD, a novel dissimilarity measurement was performed for diagnosis, represented as interval numbers. The space alignment technique eliminated the feature space divergence caused by different symptoms between two diseases, and the development of collective matrix factorization enabled knowledge transfer between the source and target domains. Experiments and a case study using real-world data demonstrated that ITMD outperforms four baselines and improves the accuracy of recommendations for disease risks in patients to support physicians in determining a final medical diagnosis.
Improving Domain-Adaptive Person Re-Identification by Dual-Alignment Learning With Camera-Aware Image Generation

Domain adaptation in person re-identification (re-ID) has always been challenging, especially for the lack of supervision information on the target domain. Existing methods generally introduced extra supervision by adversarial learning techniques, then added all the augmented data in the training process to optimize the re-ID model. However, the direct utilization of all the generated data not only increases additional computational cost but also ignores the potential correlation between the origin and generated data. In this article, we propose a novel dual-alignment learning framework (DAL) with camera-aware image generation to efficiently and effectively tackle this issue. Specifically, we propose a camera transfer matching module to generate additional training images with different camera styles, and construct the matching pairs with each containing a origin image and one corresponding camera transferred image. To strengthen the correlation of images for each matching pair, we align the pseudo-labels via clustering algorithm to reduce the pseudo-labels distribution discrepancy between the origin and generated images. Besides, to avoid model degeneration affected by some inaccurate pseudo-labels on unlabelled data, we maximize the mutual information to align the image feature representations of matching pair. The DAL allows us to decrease the camera variance and enhance the discrimination ability of re-ID model. Extensive experiments on three large-scale benchmarks demonstrate the superiority of DAL over state-of-the-art methods.
An entity-graph based reasoning method for fact verification

Fact verification aims to retrieve relevant evidence from a knowledge base, e.g., Wikipedia, to verify the given claims. Existing methods only consider the sentence-level semantics for evidence representations, which typically neglect the importance of fine-grained features in the evidence-related sentences. In addition, the interpretability of the reasoning process has not been well studied in the field of fact verification. To address such issues, we propose an entity-graph based reasoning method for fact verification abbreviated as RoEG, which generates the fine-grained features of evidence at the entity-level and models the human reasoning paths based on an entity graph. In detail, to capture the semantic relations of retrieved evidence, RoEG introduces the entities as nodes and constructs the edges in the graph based on three linking strategies. Then, RoEG utilizes a selection gate to constrain the information propagation in the sub-graph of relevant entities and applies a graph neural network to propagate the entity-features for reasoning. Finally, RoEG employs an attention aggregator to gather the information of entities for label prediction. Experimental results on a large-scale benchmark dataset FEVER demonstrate the effectiveness of our proposal by beating the competitive baselines in terms of label accuracy and FEVER Score. In particular, for a task of multiple-evidence fact verification, RoEG produces 5.48% and 4.35% improvements in terms of label accuracy and FEVER Score against the state-of-the-art baseline. In addition, RoEG shows a better performance when more entities are involved for fact verification.
Data literacy and management of research data – a prerequisite for the sharing of research data

Purpose The purpose of this paper is to investigate the knowledge and attitude about research data management, the use of data management methods and the perceived need for support, in relation to participants’ field of research. Design/methodology/approach This is a quantitative study. Data were collected by an email survey and sent to 792 academic researchers and doctoral students. Total response rate was 18% ( N = 139). The measurement instrument consisted of six sets of questions: about data management plans, the assignment of additional information to research data, about metadata, standard file naming systems, training at data management methods and the storing of research data. Findings The main finding is that knowledge about the procedures of data management is limited, and data management is not a normal practice in the researcher's work. They were, however, in general, of the opinion that the university should take the lead by recommending and offering access to the necessary tools of data management. Taken together, the results indicate that there is an urgent need to increase the researcher's understanding of the importance of data management that is based on professional knowledge and to provide them with resources and training that enables them to make effective and productive use of data management methods. Research limitations/implications The survey was sent to all members of the population but not a sample of it. Because of the response rate, the results cannot be generalized to all researchers at the university. Nevertheless, the findings may provide an important understanding about their research data procedures, in particular what characterizes their knowledge about data management and attitude towards it. Practical implications Awareness of these issues is essential for information specialists at academic libraries, together with other units within the universities, to be able to design infrastructures and develop services that suit the needs of the research community. The findings can be used, to develop data policies and services, based on professional knowledge of best practices and recognized standards that assist the research community at data management. Originality/value The study contributes to the existing literature about research data management by examining the results by participants’ field of research. Recognition of the issues is critical in order for information specialists in collaboration with universities to design relevant infrastructures and services for academics and doctoral students that can promote their research data management.
Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation

During the recent years, much effort has been invested in non-autoregressive neural machine translation, which appears to be an efficient alternative to state-of-the-art autoregressive machine translation on modern GPUs. In contrast to the latter where generation is sequential, the former allows generation to be parallelized across target token positions. Non-autoregressive machine translation provides a tradeoff between translation quality and inference speed, but some of the latest models have achieved impressive tradeoffs compared to autoregressive baselines. In this work, we reexamine this tradeoff and argue that autoregressive baselines can be substantially sped up without loss in accuracy. Specifically, we study autoregressive models with encoders and decoders of varied depths. Our extensive experiments show that given a sufficiently deep encoder, a single-layer autoregressive decoder can substantially outperform strong non-autoregressive models with comparable inference speed. We show that the speed disadvantage for autoregressive baselines compared to non-autoregressive methods has been overestimated in three aspects: suboptimal layer allocation, insufficient speed measurement, and lack of knowledge distillation. Our results establish a new protocol for future research toward fast, accurate machine translation.
Recommendation System for E-commerce Using Alternating Least Squares (ALS) on Apache Spark

Recommendation system can predict the ratings of users to items by leveraging machine learning algorithms. The use of recommendation systems is common in e-commerce websites now-a-days. Since enormous amounts of data including users’ click streams, purchase history, demographics, social networking comments and user-item ratings are stored in e-commerce systems databases, the volume of the data is getting bigger at high speed, and the data is sparse. However, the recommendations and predictions must be made in real time, enabling to bring enormous benefits to human beings. Apache spark is well suited for applications which require high speed query of data, transformation and analytics results. Therefore, the recommendation system developed in this research is implemented on Apache Spark. Also, the matrix factorization using Alternating Least Squares (ALS) algorithm which is a type of collaborative filtering is used to solve overfitting issues in sparse data and increases prediction accuracy. The overfitting problem arises in the data as the user-item rating matrix is sparse. In this research a recommendation system for e-commerce using alternating least squares (ALS) matrix factorization method on Apache Spark MLlib is developed. The research shows that the RMSE value is significantly reduced using ALS matrix factorization method and the RMSE is 0.870. Consequently, it is shown that the ALS algorithm is suitable for training explicit feedback data set where users provide ratings for items.
SynergyChain: A Multichain-Based Data-Sharing Framework With Hierarchical Access Control

With customization of demands and functionalities, diverse blockchain systems are integrated with IoT in different application scenarios, forming a multichain environment. Multichain interoperability has become a crucial emerging issue, i.e., different blockchain systems are difficult to interact with each other credibly and efficiently. It further leads to isolated data islands across multichain. Therefore, it is of significant importance to facilitate data sharing among multichain systems. Toward achieving this, there are two main challenges. First, data across multiple blockchains must be shared reliably to meet the tamper-proof merits of blockchain technology. Second, we must control the data access process in a fine-grained way to protect sensitive data and user privacy. This article proposes SynergyChain, a multichain framework to enable reliable data sharing with controllable data access. By aggregating the data from multiple blockchains and reorganizing it in SynergyChain, we can achieve data reliability with the verification. Meanwhile, SynergyChain provides hierarchical access control based on smart contracts, making access control automated and credible. Experiments show that SynergyChain can support data sharing reliably and efficiently and reduce data query latency compared with multichain data requesting sequentially.
The Recurrent Neural Tangent Kernel

The study of deep neural networks (DNNs) in the infinite-width limit, via the so-called neural tangent kernel (NTK) approach, has provided new insights into the dynamics of learning, the capacity of generalization, and the impact of initialization. One key DNN architecture remains to be kernelized, namely, the recurrent neural network (RNN). In this paper we introduce and study the Recurrent Neural Tangent Kernel (RNTK), which provides new insights into the behavior of overparametrized RNNs, including how different time steps are weighted by the RNTK to form the output under different initialization parameters and nonlinearity choices, and how inputs of different lengths are treated. The ability to compare inputs of different length is a key property of the RNTK that should greatly benefit practitioners. We demonstrate via a synthetic and 53 real-world data experiments that the RNTK offers significant performance gains over other kernels, including standard NTKs, across a wide array of data sets.
Visual Analytics for Electronic Health Records: A Review

The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years, several EHR-based visual analytics systems have been developed to fulfill healthcare experts’ computational and cognitive demands. In this paper, we conduct a systematic literature review to present the research papers that describe the design of EHR-based visual analytics systems and provide a brief overview of 22 systems that met the selection criteria. We identify and explain the key dimensions of the EHR-based visual analytics design space, including visual analytics tasks, analytics, visualizations, and interactions. We evaluate the systems using the selected dimensions and identify the gaps and areas with little prior work.
An approach for security evaluation and certification of a complete quantum communication system

Abstract Although quantum communication systems are being deployed on a global scale, their realistic security certification is not yet available. Here we present a security evaluation and improvement protocol for complete quantum communication systems. The protocol subdivides a system by defining seven system implementation sub-layers based on a hierarchical order of information flow; then it categorises the known system implementation imperfections by hardness of protection and practical risk. Next, an initial analysis report lists all potential loopholes in its quantum-optical part. It is followed by interactions with the system manufacturer, testing and patching most loopholes, and re-assessing their status. Our protocol has been applied on multiple commercial quantum key distribution systems to improve their security. A detailed description of our methodology is presented with the example of a subcarrier-wave system. Our protocol is a step towards future security evaluation and security certification standards.
BuTTER: BidirecTional LSTM for Food Named-Entity Recognition

In the modern era of big data, one of the biggest challenges is to find an efficient way of extracting information from unstructured data and structuring it in a form that can be interpreted and utilized by both humans and computers. In this paper, we focus on the domain of food and nutrition by introducing a Machine Learning (ML) based Named Entity Recognition (NER) method, which is a crucial step in extracting information from unstructured textual data. To the best of our knowledge, this is the first corpus-based food NER method that has been enabled by the recently published FoodBase corpus. The method is based on Bidirectional Long Short-Term Memory (BiLSTM) in conjunction with Conditional Random Fields (CRF) and Representation Learning (RL). Our experiments show that, despite the relatively small amount of annotated data, BuTTER is able to successfully identify food entities from raw text, with the best of the proposed models achieving an average macro F1 score of 0.946.
Real-world blockchain applications under the lens of the oracle problem. A systematic literature review

As they have become leading topics of meetings, events and conferences, real-world blockchain applications have turned niche literature into a vast and complex plethora of books, articles and papers. Unlike digital payments, however, real-world applications are dependent on oracles, whose roles and implications are often neglected in the literature. The presence of oracles negatively affects decentralization and trustless consensus, generating faulty thinking or overly positive expectations. This paper aims to enlighten the state of the art of real-world blockchain applications through a systematic literature review, exploiting the oracle problem as a lens of analysis. The results support the view that almost 90% of the inspected literature is biased or incomplete.
Path planning method for unmanned underwater vehicles eliminating effect of currents based on artificial potential field

Abstract To eliminate the effect of ocean currents for optimal path planning for unmanned underwater vehicles (UUVs) in the underwater environment, an intelligent algorithm is designed and proposed in this paper. The algorithm consists of two parts: an artificial potential field-based algorithm that derives the shortest path and avoids collision accidents; and an adjusting function that eliminates the effect of ocean currents. The planning results of the intelligent algorithm are presented in detail, and compared with the conventional algorithm that does not consider the effect of currents. The effectiveness of the optimised path planning method given in this paper is proved.