Reviewerly/exHarmony huggingface dataset:
Efficient point-of-interest recommendation with hierarchical attention mechanism

Personalized Point-of-Interest (POI) Recommendation is very important for application platforms based on Location Based Social Networks (LBSNs). It can assist users in making decisions to alleviate the problem of information overload, and can also improve the user experience of these platforms and advance platform operators achieve personalized and accurate advertising. However, there exist some problems of data sparseness and cold start for a single user, and it is also difficult to mine valuable long-tailed POIs, although the size of the check-in data is large. Therefore, in order to address the above problems, we propose a personalized POI Recommendation approach based on Hierarchical Attention Mechanism (HAM-POIRec) which can effectively increase data utilization. Firstly, we define the concepts of explicit features and implicit features, which pave the ideas of selecting data and computational models for POI recommendation based on machine learning. Secondly, we propose a hierarchical attention mechanism with the structure of local-to-global, which extracts contributions and mines more hidden information from individual features, combination features, and overall features. Finally, we present the Natural Language Processing (NLP)-based “User-POI” matching mechanism for the first time in the field of POI recommendation to improve the recommendation accuracy by fine-tuning the POIs predicted by the recommendation system. Extensive experiments are conducted for demonstrating that the HAM-POIRec method outperforms state-of-the-art DeepPIM method and the other comparison methods (SAE-NAD, MGMPFM and LRT), especially in predicting sequence POIs and solving cold start problem.
Enhanced Self-Perception in Mixed Reality: Egocentric Arm Segmentation and Database With Automatic Labeling

In this study, we focus on the egocentric segmentation of arms to improve self-perception in Augmented Virtuality (AV). The main contributions of this work are: i) a comprehensive survey of segmentation algorithms for AV; ii) an Egocentric Arm Segmentation Dataset (EgoArm), composed of more than 10, 000 images, demographically inclusive (variations of skin color, and gender), and open for research purposes. We also provide all details required for the automated generation of groundtruth and semi-synthetic images; iii) the proposal of a deep learning network to segment arms in AV; iv) a detailed quantitative and qualitative evaluation to showcase the usefulness of the deep network and EgoArm dataset, reporting results on different real egocentric hand datasets, including GTEA Gaze+, EDSH, EgoHands, Ego Youtube Hands, THU-Read, TEgO, FPAB, and Ego Gesture, which allow for direct comparisons with existing approaches using color or depth. Results confirm the suitability of the EgoArm dataset for this task, achieving improvements up to 40% with respect to the baseline network, depending on the particular dataset. Results also suggest that, while approaches based on color or depth can work under controlled conditions (lack of occlusion, uniform lighting, only objects of interest in the near range, controlled background, etc.), deep learning is more robust in real AV applications.
Semi-Supervised Adversarial Variational Autoencoder

We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE) by injecting an adversarial learning. Instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we use a consistency principle for deep features. The main contributions are threefold. Firstly, our approach perfectly combines the two models, i.e., GAN and VAE, and thus improves the generation and reconstruction performance of the VAE. Secondly, the VAE training is done in two steps, which allows to dissociate the constraints used for the construction of the latent space on the one hand, and those used for the training of the decoder. By using this two-step learning process, our method can be more widely used in applications other than image processing. While training the encoder, the label information is integrated to better structure the latent space in a supervised way. The third contribution is to use the trained encoder for the consistency principle for deep features extracted from the hidden layers. We present experimental results to show that our method gives better performance than the original VAE. The results demonstrate that the adversarial constraints allow the decoder to generate images that are more authentic and realistic than the conventional VAE.
Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing

Background Heart failure (HF) is a major cause of morbidity and mortality. However, much of the clinical data is unstructured in the form of radiology reports, while the process of data collection and curation is arduous and time-consuming. Purpose We utilized a machine learning (ML)-based natural language processing (NLP) approach to extract clinical terms from unstructured radiology reports. Additionally, we investigate the prognostic value of the extracted data in predicting all-cause mortality (ACM) in HF patients. Materials and methods This observational cohort study utilized 122,025 thoracoabdominal computed tomography (CT) reports from 11,808 HF patients obtained between 2008 and 2018. 1,560 CT reports were manually annotated for the presence or absence of 14 radiographic findings, in addition to age and gender. Thereafter, a Convolutional Neural Network (CNN) was trained, validated and tested to determine the presence or absence of these features. Further, the ability of CNN to predict ACM was evaluated using Cox regression analysis on the extracted features. Results 11,808 CT reports were analyzed from 11,808 patients (mean age 72.8 ± 14.8 years; 52.7% (6,217/11,808) male) from whom 3,107 died during the 10.6-year follow-up. The CNN demonstrated excellent accuracy for retrieval of the 14 radiographic findings with area-under-the-curve (AUC) ranging between 0.83–1.00 (F1 score 0.84–0.97). Cox model showed the time-dependent AUC for predicting ACM was 0.747 (95% confidence interval [CI] of 0.704–0.790) at 30 days. Conclusion An ML-based NLP approach to unstructured CT reports demonstrates excellent accuracy for the extraction of predetermined radiographic findings, and provides prognostic value in HF patients.
A Survey of Orthographic Information in Machine Translation

Abstract Machine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the linguistic difference and variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthography’s influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under-resourced languages. We discuss different types of machine translation and demonstrate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts using cognate information at different levels of machine translation and the lessons that can be drawn from this. Additionally, multilingual neural machine translation of closely related languages is given a particular focus in this survey. This article ends with a discussion of the way forward in machine translation with orthographic information, focusing on multilingual settings and bilingual lexicon induction.
Virtual Reality in Science Education: a Descriptive Review

The term “Virtual Reality” currently refers to a profound sensory immersion of the user in a synthetically generated virtual environment. It is foreseen that virtual reality will gain a substantial role in the instruction of science. In this literature review, the purpose was to investigate the research on the utilization of virtual reality in the science education context, according to several criteria. The articles published in peer-reviewed journals and academic conferences/symposiums that are available in the databases of ERIC, WOS, and Google Scholar have been reviewed. Consequently, a total of 30 eligible articles reviewed and findings presented under every respective criterion. Partially, findings revealed the dominance of journal article type publications, the USA and Turkey found to be most prominent origins, experimental studies being preferred mostly, undergraduate students and pre-service teachers were the most studied groups, the contexts of the studies were prominently general, and the learning outcomes investigated mostly.
Three Adaptive Sub-Histograms Equalization Algorithm for Maritime Image Enhancement

According to maritime image histograms’ statistic and analysis, the histogram of pure maritime image obeys Gaussian distribution approximately, thus Three Adaptive Sub-histograms Equalization (TASHE) algorithm for maritime image enhancement is proposed in this paper. First, the characteristics of pure maritime image’s histogram are studied, then the adaptive threshold’s optimal selection strategy for the histogram’s division is discussed, finally the implement of three sub-histograms is described. This paper employs visible gray maritime image, visible color maritime image and infrared maritime image to verify the enhancement algorithm’s effectiveness and robustness, the experimental results show that TASHE algorithm can not only keep the maritime image’s mean brightness and naturalness, but also improve the maritime image’s contrast without the noise and artifacts. The objective image quality assessment also indicates that TASHE algorithm can improve the original maritime image’s Enhancement Measure by Entropy (EME) value, furthermore, when a maritime image is pre-processed by TASHE algorithm, the maritime target’s Detection Rate (DR) can be improved.
Blockchain-Enabled Platforms: Challenges and Recommendations

Not even a tenth of blockchain-enabled platforms survive their first anniversary.The volatility of cryptomarkets has brought negative attention and led some to question the applicability of blockchain technology.This paper argues that the challenges for startups and incumbents behind these platforms are numerous, and that the speculative bubble around cryptocurrencies is only one of them.Blockchain still needs to demonstrate fully its disruptive potential and so far, entrepreneurs have not managed to significantly impact incumbents' market shares.This transitory period requires incumbents to let go of traditional control mech-anisms, and startups to scale down their global decentralised hopes.Indeed, whilst the technology can indeed scale fast, starting in a controlled market and managing growth is a counterintuitive but essential strategy for blockchain-enabled platforms to implement.Given the diverging nature of the technology, at present at least, the combined shortage of skills in blockchain and security, and the trust blockchain is built on, rushing to the global market is high risk.Nonetheless, given the potential returns, the risk appetite is high and both entrepreneurs and corporate executives share unrealistic expectations about a technology they cannot fully understand since it has not yet converged.In light of the above, this article identifies the main challenges faced when building blockchainenabled platforms and provides recommendations for startups and incumbents to overcome these.In order to reach these conclusions, the information obtained from twenty semi-structured interviews with leading actors in the field has been fundamental.
A Study on CP-ABE-Based Medical Data Sharing System with Key Abuse Prevention and Verifiable Outsourcing in the IoMT Environment

Recent developments in cloud computing allow data to be securely shared between users. This can be used to improve the quality of life of patients and medical staff in the Internet of Medical Things (IoMT) environment. However, in the IoMT cloud environment, there are various security threats to the patient's medical data. As a result, security features such as encryption of collected data and access control by legitimate users are essential. Many studies have been conducted on access control techniques using ciphertext-policy attribute-based encryption (CP-ABE), a form of attribute-based encryption, among various security technologies and studies are underway to apply them to the medical field. However, several problems persist. First, as the secret key does not identify the user, the user may maliciously distribute the secret key and such users cannot be tracked. Second, Attribute-Based Encryption (ABE) increases the size of the ciphertext depending on the number of attributes specified. This wastes cloud storage, and computational times are high when users decrypt. Such users must employ outsourcing servers. Third, a verification process is needed to prove that the results computed on the outsourcing server are properly computed. This paper focuses on the IoMT environment for a study of a CP-ABE-based medical data sharing system with key abuse prevention and verifiable outsourcing in a cloud environment. The proposed scheme can protect the privacy of user data stored in a cloud environment in the IoMT field, and if there is a problem with the secret key delegated by the user, it can trace a user who first delegated the key. This can prevent the key abuse problem. In addition, this scheme reduces the user's burden when decoding ciphertext and calculates accurate results through a server that supports constant-sized ciphertext output and verifiable outsourcing technology. The goal of this paper is to propose a system that enables patients and medical staff to share medical data safely and efficiently in an IoMT environment.
ClothFace: A Batteryless RFID-Based Textile Platform for Handwriting Recognition

This paper introduces a prototype of ClothFace technology, a battery-free textile-based handwriting recognition platform that includes an e-textile antenna and a 10 × 10 array of radio frequency identification (RFID) integrated circuits (ICs), each with a unique ID. Touching the textile platform surface creates an electrical connection from specific ICs to the antenna, which enables the connected ICs to be read with an external UHF (ultra-haigh frequency) RFID reader. In this paper, the platform is demonstrated to recognize handwritten numbers 0-9. The raw data collected by the platform are a sequence of IDs from the touched ICs. The system converts the data into bitmaps and their details are increased by interpolating between neighboring samples using the sequential information of IDs. These images of digits written on the platform can be classified, with enough accuracy for practical use, by deep learning. The recognition system was trained and tested with samples from six volunteers using the platform. The real-time number recognition ability of the ClothFace technology is demonstrated to work successfully with a very low error rate. The overall recognition accuracy of the platform is 94.6% and the accuracy for each digit is between 91.1% and 98.3%. As the solution is fully passive and gets all the needed energy from the external RFID reader, it enables a maintenance-free and cost-effective user interface that can be integrated into clothing and into textiles around us.
An Optimized Digital Watermarking Scheme Based on Invariant DC Coefficients in Spatial Domain

Digital watermarking has become an essential and important tool for copyright protection, authentication, and security of multimedia contents. It is the process of embedding a watermark in the multimedia content and its extraction. Block-based discrete cosine transform (DCT) is a widely used method in digital watermarking. This paper proposes a novel blind image watermarking scheme developed in the spatial domain by quantization of invariant direct current (DC) coefficients. The cover image is redistributed and divided into non-overlapped square blocks and then the DC coefficients invariant to rotation, row and column flip operations, without utilization of the DCT transform, are directly calculated in the spatial domain. Utilizing the quantization parameter and watermark information, the modified DC coefficients and the difference between DC and modified DC coefficients are calculated to directly modify the pixel values to embed watermark bits in the spatial domain instead of the DCT domain. Optimal values of the quantization parameter, which plays a significant role in controlling the tradeoff between robustness and invisibility, are calculated through differential evolution (DE), the optimization algorithm. Experimental results, compared with the latest similar schemes, demonstrate the advantages of the proposed scheme.
Deep learning-based question answering system for intelligent humanoid robot

Abstract Background The development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action. Findings The question from the user will be processed using deep learning, and the result will be compared to the knowledge base on the system. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF). Conclusions Our evaluation indicates that using RNN based encoder with BiDAF gives a higher score, than CNN encoder with the BiDAF. Based on our experiment, our model get 82.43% F1 score and the RNN based encoder will give a higher EM/F1 score than using the CNN encoder.
A Methodological Approach for Mining the User Requirements Using Apriori Algorithm

Users of enterprise software are multiple, and their requirements are diverse. Often their specifications are masked by mundane details and at times are vague too. Acknowledging these complexities in requirements engineering, the paper proposes a multistage methodological approach based on Apriori algorithm, a data mining technique. It extracts useful information from the given data on the criteria of mutual association and sufficient frequency. The user requirements captured through interviews and brainstorming are pre-processed for eliminating unnecessary stop words and developing a uniform structure of small stories. Mutual association and occurrence of the requirements are represented through association rules and rule metrics, for example, ‘Lift', ‘Support', and ‘Confidence'. The requirements having strong and moderate association are placed in ‘Top Priority List'; those with nominal, weak, or nil association are placed in ‘Low Priority List'. Gap analysis is employed to validate the defined requirements with respect to stakeholders' expectations. The complete and correct lists of requirements significantly influence the client satisfaction, software development process, and its eventual success.
Traceability and ownership claim of data on big data marketplace using blockchain technology

In the era of big data, modern data marketplaces have received much attention as they allow not only large enterprises but also individuals to trade their data. This new paradigm makes the data prone to various threats, including piracy, illegal reselling, tampering, illegal redistribution, ownership claiming, forgery, theft, misappropriation, etc. Although digital watermarking is a promising technique to address the above-mentioned challenges, the existing solutions in the literature are deemed to be incompetent in big data scenarios due to the following factors: V's of big data, involvement of multiple owners, incremental watermarking, large cover-size and limited watermark-capacity, non-interference, etc. In this paper, we propose a novel big data watermarking technique that leverages the power of blockchain technology and provides a transparent immutable audit trail for data movement in big data monetizing scenarios. In this context, we address all the crucial challenges mentioned above. We present a prototype implementation of the system as a proof of concept using Solidity on Ethereum platform, and we perform experimental evaluation to demonstrate its feasibility and effectiveness in terms of execution gas costs. To the best of our knowledge, this is the first proposal which deals with watermarking issues in the context of big data.
A note on complex fuzzy subfield

In this paper, we introduce idea of complex fuzzy subfield and discuss its various algebraic aspects. We prove that every complex fuzzy subfield generate two fuzzy fields and shows that intersection of two complex fuzzy subfields is also complex fuzzy subfields. We also present the concept of level subsets of complex fuzzy subfield and shows that level subset of complex fuzzy subfield form subfield. Furthermore, we extend this idea to define the notion of the direct product of two complex fuzzy subfields and also investigate the homomorphic image and inverse image of complex fuzzy subfield.
Image Object Extraction Based on Semantic Detection and Improved K-Means Algorithm

Object extraction is an important tool in many applications within the image processing and computer vision communities.You Only Look Once version 3 (YOLOv3) has been extensively applied to many fields as a state-of-the-art technique for object semantic detection.Despite its numerous characteristics, YOLOv3 has to be combined with appropriate image segmentation technologies to achieve effective 2D object extraction in real-time monitoring, robot navigation, and target search.In this article, the K-means algorithm is applied to the segmentation of depth images.Considering the inherent sensitivity to the randomness of the initial cluster center and the uncertainty of cluster number K in the initialization phase of the K-means algorithm, this article proposes a new method that combines the semantic image information with the image depth information.Specifically, this method proposed to pre-classify the center depth of the object to determine the appropriate value of K required in the K-means algorithm.At the same time, the proposed algorithm improves the selection of the initial center via the maximin method.This article introduces a multi-parameter extraction method to enable to correctly identify the object of interest after image segmentation.The technique considers three parameters to achieve this: i) the elements of size, ii) the connected domain, and iii) the diagonal detection.Experiments using open-source datasets demonstrate that the average processing time and the segmentation accuracy of the improved K-means algorithm are 20.36%faster and 3.12% higher than the conventional K-means algorithm, respectively.The extraction accuracy of the proposed method is 6.69% higher than that of the SuperCut extraction method.