Reviewerly/exHarmony huggingface dataset:
State-of-the-Art in Automated Story Generation Systems Research

This paper presents a review of research works from the last several years in automated story generation systems. These systems are categorised into interactive story generation systems and non-Interactive story generation systems. Interactive systems are those that collaborate with a user/author during the process of creating and/or executing the story. The extent of user interaction varies across systems but remains an integral part of the creation and/or the unfolding of the story. Non-Interactive systems concentrate on complete automation of the creative process involved in narrative generation to create diverse and interesting stories. Interactive story generators specifically designed for video game narratives are reviewed as a separate sub-class of interactive story generation systems. Also reviewed are the methods used for evaluation of story generation systems as a way to explore the possibility of having standard methods of evaluation within the research community. The paper includes a discussion of trends and directions of the research discipline.
Aiding Clinical Triage with Text Classification

SNS24 is a telephone service for triage, counselling, and referral service provided by the Portuguese National Health Service. Currently, following the predefined 59 Clinical Pathways, the selection of the most appropriate one is manually done by nurses. This paper presents a study on using automatic text classification to aid on the clinical pathway selection. The experiments were carried out on 3 months calls data containing 269,669 records and a selection of the best combination of ten text representations and four machine learning algorithm was pursued by building 40 different models. Then, fine-tuning of the algorithm parameters and the text embedding model were performed achieving a final accuracy of 78.80% and F1 of 78.45%. The best setup was then used to calculate the accuracy of the top-3 and top-5 most probable clinical pathways, reaching values of 94.10% and 96.82%, respectively. These results suggest that using a machine learning approach to aid the clinical triage in phone call services is effective and promising.
Personalizing Handwriting Recognition Systems with Limited User-Specific Samples

Personalization of handwriting recognition is still an understudied area due to the lack of a comprehensive dataset. We collect a dataset of 37,000 words handwritten by 40 writers that we make publicly available. We investigate the impact of personalization on recognition by training a baseline recognition model and retraining it using our dataset. After controlling that our model really adapts to the personal handwriting style and not just to the overall domain, we show that personalization in general requires several hundred samples to be effective. However, we show that the choice of transfer samples is important and that we can quickly personalize a model with a limited number of samples. We also examine whether we can detect adversarial behavior trying to reduce recognition performance.
ASAR 2021 Competition on Online Arabic Word Recognition

This paper presents the Online Arabic words recognition competition (OAWRC 2021) held at ASAR 2021 workshop in conjuction with ICDAR 2021. This competition uses the ADAB database, which is a database of Arabic handwritten words. Three groups with four systems are participating in the competition this year. A brief description of participating systems and the experimental results is doing. The systems were tested on unknown set to the participants of 100 labels. In particular, we wish to investigate and compare participating methods that can be robust against competition challenges. The main goal of this competition is to encourage the research in Arabic script and the development of Arabic handwritten systems. Within good recognition rate, the competition has two new challenges: working with small training set and being robust against noise. The evaluation process of participating systems adapts various metrics such as the recognition rate, the speed, etc. All systems produce good results. The winner and his runner-up are very close while they use different methods.
Transmission Lines Monitoring Based on Convolution Neural Network and Edge Computation

Transmission lines ordinary inspection is an important work for power companies to ensure its safety and stability. With the development of ubiquitous power Internet of Things, realizing intelligent analysis of transmission lines by edge computing is the development direction of smart grid. Convolutional neural networks combining with an edge computation platform are used for feature extraction in this paper. The network structure comprehensively considers some features, such as complex image scenes, the difficulty in extracting hidden dangers type features, and large target size variation spans. The proposed model uses multi-scale prediction and feature fusion technology to detect common hidden dangers in the transmission lines surroundings. Experiments show that the mean average precision (mAP) of our model is 89.1%, and the detection speed is about 1.25 frames per second (FPS). The higher detection accuracy lays a solid foundation for the image intelligent processing algorithm of transmission lines and the edge computation platform.
Facemask Detection Based on Double Convolutional Neural Networks

Background: The study on facemask detection is of great significance because facemask detection is difficult, and the workload is heavy in places with a large number of people during the COVID-19 outbreak. Objective: The study aims to explore new deep learning networks that can accurately detect facemasks and improve the network's ability to extract multi-level features and contextual information. In addition, the proposed network effectively avoids the interference of objects like masks. The new network could eventually detect masks wearers in the crowd. Method: A Multi-stage Feature Fusion Block (MFFB) and a Detector Cascade Block (DCB) are proposed and connected to the deep learning network for facemask detection. The network's ability to obtain information improves. The network proposed in the study is Double Convolutional Neural Networks (CNN) called DCNN, which can fuse mask features and face position information. During facemask detection, the network extracts the featural information of the object and then inputs it into the data fusion layer. Results: The experiment results show that the proposed network can detect masks and faces in a complex environment and dense crowd. The detection accuracy of the network improves effectively. At the same time, the real-time performance of the detection model is excellent. Conclusion: The two branch networks of the DCNN can effectively obtain the feature and position information of facemasks. The network overcomes the disadvantage that a single CNN is susceptible to the interference of the suspected mask objects. The verification shows that the MFFB and the DCB can improve the network's ability to obtain object information, and the proposed DCNN can achieve excellent detection performance.
Negative-supervised capsule graph neural network for few-shot text classification

Few-shot text classification aims to learn a classifier from very few labeled text data. Existing studies on this topic mainly adopt prototypical networks and focus on interactive information between support set and query instances to learn generalized class prototypes. However, in the process of encoding, these methods only pay attention to the matching information between support set and query instances, and ignore much useful information about intra-class similarity and inter-class dissimilarity between all support samples. Therefore, in this paper we propose a negative-supervised capsule graph neural network (NSCGNN) which explicitly takes use of the similarity and dissimilarity between samples to make the text representations of the same type closer with each other and the ones of different types farther away, leading to representative and discriminative class prototypes. We firstly construct a graph to obtain text representations in the form of node capsules, where both intra-cluster similarity and inter-cluster dissimilarity between all samples are explored with information aggregation and negative supervision. Then, in order to induce generalized class prototypes based on those node capsules obtained from graph neural network, the dynamic routing algorithm is utilized in our model. Experimental results demonstrate the effectiveness of our proposed NSCGNN model, which outperforms existing few-shot approaches on three benchmark datasets.
Using correction parameters to improve real-time video interpolation in low-cost VLSI implementation

This paper introduces a relative-contrast-based algorithm to improve VLSI real-time video interpolation image quality. A relative-contrast-based adaptive interpolation correction factor wi is formed to correct linear interpolation algorithm. A LUT is integrated in proposed design to simplify the logarithmic process which is the key procedure in wi generation. Proposed algorithm improved the image quality and decline resource requirements compared with similar efforts. In comprehensive, the VLSI implementation performance shows predictably image quality improvements with sensible area cost. The chip test result shows interpolation function can support up to 1920x1080 at 60FPS, both scaler up and down.
A new extension of von Mises-Fisher distribution

Spherical distributions, including the von Mises-Fisher density, have received a great attention in the literature because of their usefulness to model circular data lying on the unit sphere. However, there is a paucity of research on proposing spherical densities possing multimode tuned with a single parameter. To fill in this gap, we extend von Mises-Fisher distribution to construct a new density. Moreover, some of the important statistical properties of the proposed distribution including the estimation of parameters are highlighted. To evaluate the performance of the proposed distribution, some simulation studies and analyzing three real-life examples are presented.
Goal and Elite-Data-Driven Anthropomorphic Learning for Streaming-Image Analytics

With the rise of the Internet of Things (IoT), many edge cameras (smart cameras that leverage edge computing) have provided real-time streaming-image analytics using anthropomorphic learning. However, previous anthropomorphic-learning studies did not allow the setting of customized goals to filter out concerned features during online model learning. In addition, when any concept drift occurs, the existing studies using an edge camera cannot adapt efficiently using only representative images. Our study therefore proposes solutions to the above issues based on the self-regulated learning framework, which divides a learning procedure into before, during, and after stages to improve overall learning performance, just like human beings. In the before learning stage of a model on an edge camera, we propose a generative adversarial network (GAN)-based customized feature filtering to screen out users’ concerned features before real-time model learning of image analytics. Next, during the learning stage of the model, we also propose keyframe-oriented model adaptation that can effectively respond to concept drift while reducing image storage space on an edge camera. The experimental results show that customized concerned feature filtering can improve the adversarial effect of the concerned features by 24.26 percent on average, meaning it can avoid using unwanted features from learning an undesirable model. The keyframe-oriented model adaptation can accelerate the overall adjustment time by 52 percent and save the required storage space by 54.7 percent at the cost of compromising precision by 2 percent at most.
Colocationist Answers to the Grounding Problem

Abstract According to colocationism , two different material objects can be colocated during their entire careers. The typical example is that of a statue and the lump of clay out of which it is made, both of which start to exist and cease to exist at exactly the same time. One of the main problems for colocationism is the grounding problem . Recently, several attractive colocationist answers to the problem have been formulated. In this paper I analyse the proposals by Kit Fine, Kathrin Koslicki, Noël Saenz and Catherine Sutton and conclude that all of them have problematic, unsatisfactory consequences, and this constitutes a strong reason against them. That, in turn, shows, I think, the difficulty of finding a satisfactory colocationist answer to the grounding problem, which continues to be a fundamental difficulty for the position.
NTable: A Dataset for Camera-Based Table Detection

Comparing with raw textual data, information in tabular format is more compact and concise, and easier for comparison, retrieval, and understanding. Furthermore, there are many demands to detect and extract tables from photos in the era of Mobile Internet. However, most of the existing table detection methods are designed for scanned document images or Portable Document Format (PDF). And tables in the real world are seldom collected in the current mainstream table detection datasets. Therefore, we construct a dataset named NTable for camera-based table detection. NTable consists of a smaller-scale dataset NTable-ori, an augmented dataset NTable-cam, and a generated dataset NTable-gen. The experiments demonstrate deep learning methods trained on NTable improve the performance of spotting tables in the real world. We will release the dataset to support the development and evaluation of more advanced methods for table detection and other further applications in the future.
Pre-Training Convolution Network for Crowd Counting

Image crowd density estimation is widely used in video surveillance, traffic surveillance and public safety. Recently, the convolutional neural network based approach has shown better results in crowd counting than the traditional approach. However, there are still a lot of limitations and difficulties in practical application :large-range number of people, changeable environment result in the current methods fail work well. In addition, Due to the lack of practical data, many methods suffer from over-fitting in varying degrees. To solve these two problems, first, we built a data collector and annotator, which can generate a large number of synthetic crowd scenes without any labor cost, and annotate them automatically. Secondly, we use pre-training method to pre-train on the synthetic dataset, and then fine-tune with the real data to effectively improve the performance of the model in the real scene. Extensive experiments show that the method is achieves the state-of-the-art performance on four real datasets.
Research on English Cloud Classroom Blended Teaching Mode Based on Mobile APP

To promote English learning with the development of blended learning and mobile learning, this paper designs and develops a blended English learning platform based on mobile APP. This paper also designs a mobile learning platform under Android environment. The background management system adopts the latest SSM framework of Java EE, which effectively solves the problem of server-side development. Client users login using OAuth authentication, and XML data using Sax parsing. At the same time, it can realize the safe storage of data and client fast access. The classroom communication module of such system is embedded with Baidu's cloud push technology to realize the search engine. The final system test analysis shows that the mobile learning system has friendly interface, convenient operation, reliable and stable performance.
Vision based quality control of composite components: considerations about measurement accuracy

Abstract In this paper a methodology is discussed concerning the measurement of yarn’s angle of a reinforced polypropylene matrix used in the production of automotive components. The measurement method is based on a vision system and advanced processing of images in order to evaluate the geometrical parameters of interest; the accuracy of measurement is a mandatory requirement, in order to assess the simulation approach for thermoplastic process optimization. Many aspects influencing the whole accuracy of the method have been identified and their effect evaluated, of both geometrical and optical type, allowing to perform angle measurements of the fiber angle with a whole accuracy in the order of a few degrees. By this way both local and extended defects can be identified in a reliable way also with reference to components of complex geometry. According to these results, accurate measurements of angle allows us to both validate the simulation of the thermoplastic process and to give suggestions for process improvement of fiber glass components of complex geometry.
Shared-view and specific-view information extraction for recommendation

In various recommender systems, ratings and reviews are the main information to show user preferences. However, recommendation models that only use ratings, such as collaborative filtering, are vulnerable to data sparsity. And models only using review information will also suffer from the sparsity of reviews. On one hand, most ratings and reviews are interrelated and complementary, reviews may explain why a user gives a high or low rating to an item. On the other hand, ratings and reviews are numerical and textual information, respectively, and they reflect the preference of the user from a coarse-grained level and a fine-grained level A user may comment positively about some aspects of an item, even he gives a very low score to this item. There are specific information among each of them because of their heterogeneity. Therefore, it is possible to learn more accurate representation of users and items by effectively integrating ratings and text reviews from different views, that is, shared-view and specific-view. In this paper, we propose a Shared-view and Specific-view Information extraction model for Recommendation (SSIR), which integrates the information from reviews and interaction matrix to predict ratings Our model has two key components, including shared-view information extraction and specific-view exploitation. From the perspective of shared-view, SSIR jointly minimizes the loss of confusion adversarial and rating prediction loss to extract the shared information from reviews and user–item interaction matrix. For the specific-view part, SSIR applies orthogonal constraints on shared-view and specific-view modules to extract the discriminative features from reviews and interaction data. We fuse the features extracted from these two views to predict the final ratings. In addition, we use auxiliary reviews to deal with the sparsity problem of reviews. Experimental results on eight datasets show the effectiveness and robustness of our method, which could adapt to the recommendation scenarios with fewer reviews and ratings.
Forecasting Time-aware Nonfunctional Service Attributes Using Deep Learning

Service Oriented Architecture (SOA), as a new architecture paradigm, has been dramatically developed. A growing number of Web services have been developed and published on the Web, which increases the burden of service selection in system construction. Nonfunctional attributes of services have an essential impact on service selection. Users may observe the dynamic nonfunctional property values of different web services in various locations due to the dynamic attributes of various web services. How to accurately predict user perception of nonfunctional attributes of services has become an important problem in services computing. Towards this issue, this paper improves the personalized LSTM model by adding the attention mechanism to obtain more accurate vectorization representations of users and services, so as to better forecast missing nonfunctional service attributes. The proposed approach is evaluated on a real-world dataset, and experiment results show its feasibility.
Machine Learning Applied to K-Bentonite Geochemistry for Identification and Correlation: The Ordovician Hagan K-Bentonite Complex Case Study

Altered tephras (K-bentonites) are of great importance for calibration of the geologic time scale, for local, regional, and global correlations, and paleoenvironmental reconstructions. Thus, definitive identification of individual tephras is critical. Single crystal geochemistry has been used to differentiate tephra layers, and apatite is one of the phenocrysts commonly occurring in tephras that has been widely used. Here, we use existing and newly acquired analytical datasets (electron probe micro-analyzer [EPMA] data and laser ablation ICP-MS [LA-ICP-MS] data, respectively) of apatite in several Ordovician K-bentonites that were collected from localities about 1200 km apart (Minnesota/Iowa/Wisconsin and Alabama, United States) to test the use of machine-learning (ML) techniques to identify with confidence individual tephra layers. Our results show that the decision tree based on EPMA data uses the elemental concentration patterns of Mg, Mn, and Cl, consistent with previous studies that emphasizes the utility of these elements for distinguishing Ordovician K-bentonites. Differences in the experimental setups of the analyses, however, can lead to offsets in absolute elemental concentrations that can have a significant impact on the correct identification and correlation of individual K-bentonite beds. The ML model using LA-ICP-MS data was able to identify several K-bentonites in the southern Appalachians and establish links to K-bentonites samples from the Upper Mississippi Valley. Furthermore, the ML model identified individual layers of multiphase eruptions, thus illustrating very well the great potential of applying ML techniques to tephrochronology.
Application of Blockchain and Internet of Things to Ensure Tamper-Proof Data Availability for Food Safety

The food supply chain plays a vital role in human health and food prices. Food supply chain inefficiencies in terms of unfair competition and lack of regulations directly affect the quality of human life and increase food safety risks. This work merges Hyperledger Fabric, an enterprise-ready blockchain platform with existing conventional infrastructure, to trace a food package from farm to fork using an identity unique for each food package while keeping it uncomplicated. It keeps the records of business transactions that are secured and accessible to stakeholders according to the agreed set of policies and rules without involving any centralized authority. This paper focuses on exploring and building an uncomplicated, low-cost solution to quickly link the existing food industry at different geographical locations in a chain to track and trace the food in the market.