Reviewerly/exHarmony huggingface dataset:
Py4JFML: A Python wrapper for using the IEEE Std 1855-2016 through JFML

JFML is an open source Java library aimed at facilitating interoperability of fuzzy systems by implementing the IEEE Std 1855-2016 - the IEEE Standard for Fuzzy Markup Language (FML) that is sponsored by the IEEE Computational Intelligence Society. We developed a Python wrapper for JFML that enables to use all the functionalities of JFML through a Python 3.x module. The bridge between Python and Java is accomplished through the use of the Py4J framework. As a result, the possibility of using the IEEE standard for representing fuzzy systems is enlarged to a wider community of developers and knowledge engineers, with minimal code redundancy. Experiments show full interoperability between Python programs and JFML without any tangible overhead. We illustrate the use of Py4JFML in a beer style classification case study.
Patch Transformer for Multi-tagging Whole Slide Histopathology Images

Automated whole slide image (WSI) tagging has become a growing demand due to the increasing volume and diversity of WSIs collected nowadays in histopathology. Various methods have been studied to classify WSIs with single tags but none of them focuses on labeling WSIs with multiple tags. To this end, we propose a novel end-to-end trainable deep neural network named Patch Transformer which can effectively predict multiple slide-level tags from WSI patches based on both the correlations and the uniqueness between the tags. Specifically, the proposed method learns patch characteristics considering (1) patch-wise relations through a patch transformation module and (2) tag-wise uniqueness for each tagging task through a multi-tag attention module. Extensive experiments on a large and diverse dataset consisting of 4,920 WSIs prove the effectiveness of the proposed model.
Deep Global Generalized Gaussian Networks

Recently, global covariance pooling (GCP) has shown great advance in improving classification performance of deep convolutional neural networks (CNNs). However, existing deep GCP networks compute covariance pooling of convolutional activations with assumption that activations are sampled from Gaussian distributions, which may not hold in practice and fails to fully characterize the statistics of activations. To handle this issue, this paper proposes a novel deep global generalized Gaussian network (3G-Net), whose core is to estimate a global covariance of generalized Gaussian for modeling the last convolutional activations. Compared with GCP in Gaussian setting, our 3G-Net assumes the distribution of activations follows a generalized Gaussian, which can capture more precise characteristics of activations. However, there exists no analytic solution for parameter estimation of generalized Gaussian, making our 3G-Net challenging. To this end, we first present a novel regularized maximum likelihood estimator for robust estimating covariance of generalized Gaussian, which can be optimized by a modified iterative re-weighted method. Then, to efficiently estimate the covariance of generaized Gaussian under deep CNN architectures, we approximate this re-weighted method by developing an unrolling re-weighted module and a square root covariance layer. In this way, 3GNet can be flexibly trained in an end-to-end manner. The experiments are conducted on large-scale ImageNet-1K and Places365 datasets, and the results demonstrate our 3G-Net outperforms its counterparts while achieving very competitive performance to state-of-the-arts.
A Novel Technique for Effective Image Gallery Search using Content Based Image Retrieval System

Content-based image retrieval is known as one of the upcoming procedural structure that will apply various techniques of computer vision for penetrating and organizing the huge image anthology in an effective manner. It is largely accepted that with the growth of immense collection of digital images generated by fast progress in electronically storage ability and computing power, there is a rising requirement for devices and upgraded computer systems for the support of effective and efficient browsing, penetrating, and retrieval for various images from the image collections, be it be online or offline. Therefore, the basic idea of this technique is to develop such type of content-based image retrieval system that can put into operation in a large image gallery desktop application to permit competent browsing with the help of two different exploration methods respectively; retrieval by query based on image and retrieval by text or ticket. In this paper, the MPEG-7 and Edge Directivity Descriptor is used to take out the feature vectors of a particular image from the image database. An effective graphical user interface with the essential utility for the purpose of implementation of different images in the form of image gallery discussed.
Emotional editing constraint conversation content generation based on reinforcement learning

In recent years, the generation of conversation content based on deep neural networks has attracted many researchers. However, traditional neural language models tend to generate general replies, lacking logical and emotional factors. This paper proposes a conversation content generation model that combines reinforcement learning with emotional editing constraints to generate more meaningful and customizable emotional replies. The model divides the replies into three clauses based on pre-generated keywords and uses the emotional editor to further optimize the final reply. The model combines multi-task learning with multiple indicator rewards to comprehensively optimize the quality of replies. Experiments shows that our model can not only improve the fluency of the replies, but also significantly enhance the logical relevance and emotional relevance of the replies.
An improved water wave optimisation algorithm enhanced by CMA-ES and opposition-based learning

Water Wave Optimisation algorithm (WWO) is a new swarm-based metaheuristic inspired by shallow wave models for global optimisation. In this paper, an enhanced WWO, which combines with multiple assistant strategies (EWWO), is proposed. First, the random opposition-based learning (ROBL) mechanism is introduced to generate the initial population with high quality. Second, a new modified operation is designed and embedded into propagation operation to balance the global exploration and the local exploitation. Third, the covariance matrix self-adaptation evolution strategy (CMA-ES) is employed by the refraction operation to further strengthen the local exploitation. Furthermore, the diversity of the population is maintained in the evolution process by using a crossover operator. The experiment results based on CEC 2017 benchmarks indicate that the EWWO outperforms the state-of-the-art variant algorithms of the WWO and the standard WWO.
Fine-Grained Video Captioning via Graph-based Multi-Granularity Interaction Learning

Learning to generate continuous linguistic descriptions for multi-subject interactive videos in great details has particular applications in team sports auto-narrative. In contrast to traditional video caption, this task is more challenging as it requires simultaneous modeling of fine-grained individual actions, uncovering of spatio-temporal dependency structures of frequent group interactions, and then accurate mapping of these complex interaction details into long and detailed commentary. To explicitly address these challenges, we propose a novel framework Graph-based Learning for Multi-Granularity Interaction Representation (GLMGIR) for fine-grained team sports auto-narrative task. A multi-granular interaction modeling module is proposed to extract among-subjects' interactive actions in a progressive way for encoding both intra- and inter-team interactions. Based on the above multi-granular representations, a multi-granular attention module is developed to consider action/event descriptions of multiple spatio-temporal resolutions. Both modules are integrated seamlessly and work in a collaborative way to generate the final narrative. In the meantime, to facilitate reproducible research, we collect a new video dataset from YouTube.com called Sports Video Narrative dataset (SVN). It is a novel direction as it contains 6K team sports videos (i.e., NBA basketball games) with 10K ground-truth narratives(e.g., sentences). Furthermore, as previous metrics such as METEOR (i.e., used in coarse-grained video caption task) DO NOT cope with fine-grained sports narrative task well, we hence develop a novel evaluation metric named Fine-grained Captioning Evaluation (FCE), which measures how accurate the generated linguistic description reflects fine-grained action details as well as the overall spatio-temporal interactional structure. Extensive experiments on our SVN dataset have demonstrated the effectiveness of the proposed framework for fine-grained team sports video auto-narrative.
On the Applicability of Registration Uncertainty

Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If surgeons receive inaccurately calculated registration uncertainty and misplace unwarranted confidence in the alignment solutions, severe consequences may result. For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters. The majority of existing research focuses on trying out different summary statistics as well as means to exploit them. Distinctively, in this paper, we study two rarely examined topics: (1) whether those summary statistics of the transformation distribution most informatively represent the registration uncertainty; (2) Does utilizing the registration uncertainty always be beneficial. We show that there are two types of uncertainties: the transformation uncertainty, $$U_\mathrm {t}$$ , and label uncertainty $$U_\mathrm {l}$$ . The conventional way of using $$U_\mathrm {t}$$ to quantify $$U_\mathrm {l}$$ is inappropriate and can be misleading. By a real data experiment, we also share a potentially critical finding that making use of the registration uncertainty may not always be an improvement.
AL: autogenerating supervised learning programs

We present AL, a novel automated machine learning system that learns to generate new supervised learning pipelines from an existing corpus of supervised learning programs. In contrast to existing automated machine learning tools, which typically implement a search over manually selected machine learning functions and classes, AL learns to identify the relevant classes in an API by analyzing dynamic program traces that use the target machine learning library. AL constructs a conditional probability model from these traces to estimate the likelihood of the generated supervised learning pipelines and uses this model to guide the search to generate pipelines for new datasets. Our evaluation shows that AL can produce successful pipelines for datasets that previous systems fail to process and produces pipelines with comparable predictive performance for datasets that previous systems process successfully.
(Meta)inferential levels of entailment beyond the Tarskian paradigm

In this paper we discuss the extent to which the very existence of substructural logics puts the Tarskian conception of logical systems in jeopardy. In order to do this, we highlight the importance of the presence of different levels of entailment in a given logic, looking not only at inferences between collections of formulae but also at inferences between collections of inferences—and more. We discuss appropriate refinements or modifications of the usual Tarskian identity criterion for logical systems, and propose an alternative of our own. After that, we consider a number of objections to our account and evaluate a substantially different approach to the same problem.
Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are $$1000{\times }$$ larger in size.
Digital Forensics

Considering the ever-growing ubiquity of technology, there is an associated growth in the possibility of digital devices related to a criminal investigation or civil litigation. As the variety of digital devices is increasing, the storage capacity of each is also rising exponentially. Due to the varied and large volumes of data produced, law enforcement agencies (LEAs) worldwide are facing a significant backlog of cases. This has culminated in significant delays in dealing with cases that urgently require digital forensic investigations (DFIs). It is of paramount importance that new research approaches be adopted to address such challenges. This article evaluates the existing set of circumstances surrounding the field of digital forensics (DF). The article provides two important contributions to the field of DF; it identifies and analyses the most important mid- and long-term challenges that need to be considered by LEAs. It also proposes important specific future research directions, the undertaking of which can assist LEAs in adopting a new approach to addressing these challenges.
Performance Evaluation of Topic Modeling Algorithms for Text Classification

Text Classification is a paramount task in natural language processing. Topic modeling algorithms have been used with a lot of success for text classification. We evaluate different topic modeling algorithms for two tasks: (1) Short text or sentence classification and (2) Large text or document classification. We give an extensive performance evaluation with the help of a wide range of performance metrics for three topic modeling algorithms on both of these tasks using three publicly available datasets.
Innovating Medical Image Analysis via Spatial Logics

Current computer-assisted medical imaging for the planning of radiotherapy requires high-level mathematical and computational skills. These are often paired with the case-by-case integration of highly specialised technologies. The lack of modularity at the right level of abstraction in this field hinders research, collaboration and transfer of expertise among medical physicists, engineers and technicians. The longer term aim of the introduction of spatial logics and spatial model checking in medical imaging is to provide an open platform introducing declarative medical image analysis. This will provide domain experts with a convenient and very concise way to specify contouring and segmentation operations, grounded on the solid mathematical foundations of Topological Spatial Logics. We show preliminary results, obtained using the spatial model checker VoxLogicA, for the automatic identification of specific brain tissues in a healthy brain and we discuss a selection of challenges for spatial model checking for medical imaging.
Influencing Factors of PM2.5 Pollution: Disaster Points of Meteorological Factors

A chance constrained stochastic Data Envelopment Analysis (DEA) was developed for investigating the relations between PM2.5 pollution days and meteorological factors and human activities, incorporating with an empirical study for 13 cities in Jiangsu Province (China) to illustrate the model. This approach not only admits random input and output environment, but also allows the evaluation unit to exceed the front edge under the given probability constraint. Moreover, observing the change in outcome variables when a group of explanatory variables are deleted provides an additional strategic technique to measure the effect of the remaining explanatory variables. It is found that: (1) For 2013-2016, the influencing factors of PM2.5 pollution days included wind speed, no precipitation day, relative humidity, population density, construction area, transportation, coal consumption and green coverage rate. In 2016, the number of cities whose PM2.5 pollution days was affected by construction was decreased by three from 2015 but increased according to transportation and energy utilization. (2) The PM2.5 pollution days in southern and central Jiangsu Province were primarily affected by the combined effect of the meteorological factors and social progress, while the northern Jiangsu Province was largely impacted by the social progress. In 2013-2016, at different risk levels, 60% inland cities were of valid stochastic efficiency, while 33% coastal cities were of valid stochastic efficiency. (3) The chance constrained stochastic DEA, which incorporates the data distribution characteristics of meteorological factors and human activities, is valuable for exploring the essential features of data in investigating the influencing factors of PM2.5.
Quality-guided key frames selection from video stream based on object detection

Object detection technique is widely applied in modern intelligent systems, such as pedestrian tracking, video surveillance. Key frames selection aims to select more informative frames and reduce amount of redundant information frames. Traditional methods leveraged SIFT feature, which have high key frame selection error rate. In this paper, we propose a novel key frames selection method based on object detection and image quality. Specifically, we first leverage object detector to detect object, such as pedestrian, vehicles. Then, each training frame will be assigned with a quality score, where frames contain objects have high quality score. Afterwards, we leverage CNN based AlexNet architecture for deep feature representation extraction. Our algorithm combines mutual information entropy and SURF image local features to extract key frames. Comprehensive experiments verify the feasibility of practicing the key frame extractor based on convolutional neural network by training the model, and conduct a quality assessment model study.
Crypto-currencies narrated on tweets: a sentiment analysis approach

Purpose Crypto-currencies, decentralized electronic currencies systems, denote a radical change in financial exchange and economy environment. Consequently, it would be attractive for designers and policy-makers in this area to make out what social media users think about them on Twitter. The purpose of this study is to investigate the social opinions about different kinds of crypto-currencies and tune the best-customized classification technique to categorize the tweets based on sentiments. Design/methodology/approach This paper utilized a lexicon-based approach for analyzing the reviews on a wide range of crypto-currencies over Twitter data to measure positive, negative or neutral sentiments; in addition, the end result of sentiments played a training role to train a supervised technique, which can predict the sentiment loading of tweets about the main crypto-currencies. Findings The findings further prove that more than 50 per cent of people have positive beliefs about crypto-currencies. Furthermore, this paper confirms that marketers can predict the sentiment of tweets about these crypto-currencies with high accuracy if they use appropriate classification techniques like support vector machine (SVM). Practical implications Considering the growing interest in crypto-currencies (Bitcoin, Cardano, Ethereum, Litcoin and Ripple), the findings of this paper have a remarkable value for enterprises in the financial area to obtain the promised benefits of social media analysis at work. In addition, this paper helps crypto-currencies vendors analyze public opinion in social media platforms. In this sense, the current paper strengthens our understanding of what happens in social media for crypto-currencies. Originality/value For managers and decision-makers, this paper suggests that the news and campaign for their crypto in Twitter would affect people’s perspectives in a good manner. Because of this fact, the firms, investing in these crypto-currencies, could apply the social media as a magnifier for their promotional activities. The findings steer the market managers to see social media as a predictor tool, which can analyze the market through understanding the opinions of users of Twitter.
Large-scale and adaptive service composition based on deep reinforcement learning

Service composition is a research hotspot with practical value. With the development of Web service, many Web services with the same functional attributes emerge. However, service composition optimization is still a big challenge since the complex and unstable composition environment. To solve this problem, we propose an adaptive service composition based on deep reinforcement learning, where recurrent neural network (RNN) is utilized for predicting the objective function, improving its expression and generalization ability, and effectively solving the shortcomings of traditional reinforcement learning in the face of large-scale or continuous state space problems. We leverage heuristic behavior selection strategy to divide the state set into hidden state and fully visible state. Effective simulation of hidden state space and fully visible state of the evaluation function can further improve the accuracy and efficiency of the combined results. We conduct comprehensive experiment and experimental results have shown the effectiveness of our method.
SonicSpray: A Technique to Reconfigure Permeable Mid-Air Displays

Permeable, mid-air displays, such as those using fog or water mist are limited by our ability to shape and control the aerosol and deal with two major issues: (1) the size and complexity of the system, and (2) the creation of laminar flow, to retain display quality. Here we present SonicSpray, a technique using ultrasonic Bessel beams to create reconfigurable mid-air displays. We build a prototype from low-cost, off-the-shelf parts. We explore the potential and limitations of SonicSpray to create and redirect laminar flows of fog. We demonstrate a working prototype that precisely controls laminar aerosols through only 6x6 ultrasound transducers array. We describe the implementation steps to build the device, verify the control and projection algorithm for the display, and evaluate its performance. We finally report our exploration of several useful applications, in learning, entertainment and arts.
FaceWidgets: Exploring Tangible Interaction on Face with Head-Mounted Displays

We present FaceWidgets, a device integrated with the backside of a head-mounted display (HMD) that enables tangible interactions using physical controls. To allow for near range-to-eye interactions, our first study suggested displaying the virtual widgets at 20 cm from the eye positions, which is 9 cm from the HMD backside. We propose two novel interactions, widget canvas and palm-facing gesture, that can help users avoid double vision and allow them to access the interface as needed. Our second study showed that displaying a hand reference improved performance of face widgets interactions. We developed two applications of FaceWidgets, a fixed-layout 360 video player and a contextual input for smart home control. Finally, we compared four hand visualizations against the two applications in an exploratory study. Participants considered the transparent hand as the most suitable and responded positively to our system.