Reviewerly/exHarmony huggingface dataset:
A Smart Recommender Based on Hybrid Learning Methods for Personal Well-Being Services

The main focus of the paper is to propose a smart recommender system based on the methods of hybrid learning for personal well-being services, called a smart recommender system of hybrid learning (SRHL). The essential health factor is considered to be personal lifestyle, with the help of a critical examination of various disciplines. Integrating the recommender system effectively contributes to the prevention of disease, and it also leads to a reduction in treatment cost, which contributes to an improvement in the quality of life. At the same time, there exist various challenges within the recommender system, mainly cold start and scalability. To effectively address the inefficiencies, we propose combined hybrid methods in regard to machine learning. The primary aim of this learning method is to integrate the most effective and efficient learning algorithms to examine how combined hybrid filtering can help to improve the cold star problem efficiently in the provision of personalized well-being in regard to health food service. These methods include: (1) switching among content-based and collaborative filtering; (2) identifying the user context with the integration of dynamic filtering; and (3) learning the profiles with the help of processing and screening of reflecting feedback loops. The experimental results were evaluated by using three absolute error measures, providing comparable results with other studies relative to machine learning domains. The effects of using the hybrid learning method are gathered with the help of the experimental results. Our experiments also show that the hybrid method improves accuracy by 14.61% of the average error predicted in the recommender systems in comparison to the collaborative methods, which mainly focus on the computation of similar entities.
Virtual reality-based wheelchair simulators: A scoping review

Sense of presence (SoP) has recently emerged as one of the key elements promoting the effectiveness of virtual reality-based training programs. In the context of wheelchair simulators (WSs), the effectiveness of the simulation has been sought using different perception and interaction devices, providing the end-users with different levels of SoP. We performed a scoping review searching scientific and grey literature databases with the aim of assessing the extent of published research dealing with SoP and effectiveness of WSs. Sixty-two articles, describing 29 WSs, were included in the review. In spite of promising results, the high heterogeneity of the employed technological solutions, of the training programs and of their outcomes precluded drawing definitive conclusions about the optimal solution for the enhancement of SoP and thus of WSs’ effectiveness. Future research should focus on controlled trials in order to help researchers in assessing the most suitable technologies and methodologies for the application of WSs in clinical practice.
Camera Calibration Using Gray Code

In order to determine camera parameters, a calibration procedure involving the camera recordings of a checkerboard is usually performed. In this paper, we propose an alternative approach that uses Gray-code patterns displayed on an LCD screen. Gray-code patterns allow us to decode 3D location information of points of the LCD screen at every pixel in the camera image. This is in contrast to checkerboard patterns where the number of corresponding locations is limited to the number of checkerboard corners. We show that, for the case of a UEye CMOS camera, the precision of focal-length estimation is 1.5 times more precise than when using a standard calibration with a checkerboard pattern.
My Naturewatch Camera

My Naturewatch Camera is an inexpensive wildlife camera that we designed for people to make themselves as a way of promoting engagement with nature and digital making. We aligned its development to the interests of the BBC's Natural History Unit as part of an orchestrated engagement strategy also involving our project website and outreach to social media. Since June 2018, when the BBC featured the camera on one of their Springwatch 2018 broadcasts, over 1000 My Naturewatch Cameras have been constructed using instructions and software from our project website and commercially available components, without direct contact with our studio. In this paper, we describe the project and outcomes with a focus on its success in promoting engagement with nature, engagement with digital making, and the effectiveness of this strategy for sharing research products outside traditional commercial channels.
Differentiating hate speech: a systemic discrimination approach

In this paper I develop a systemic discrimination approach to defining a narrowly construed category of 'hate speech', as speech that harms to a sufficient degree to warrant government regulation. This is important due to the lack of definitional clarity, and the extraordinarily wide usage, of the term. This article extends current literature on how hate speech can harm by identifying under what circumstances speakers have the capacity to harm, and under what circumstances targets are vulnerable to harm. It also shows how the capacity to harm can be mobile and involve the construction of new targets. Finally, it bridges the gap between conceptual understandings of hate speech and policy designed to regulate it.
Evaluating Neural Text Simplification in the Medical Domain

Health literacy, i.e. the ability to read and understand medical text, is a relevant component of public health. Unfortunately, many medical texts are hard to grasp by the general population as they are targeted at highly-skilled professionals and use complex language and domain-specific terms. Here, automatic text simplification making text commonly understandable would be very beneficial. However, research and development into medical text simplification is hindered by the lack of openly available training and test corpora which contain complex medical sentences and their aligned simplified versions. In this paper, we introduce such a dataset to aid medical text simplification research. The dataset is created by filtering aligned health sentences using expert knowledge from an existing aligned corpus and a novel simple, language independent monolingual text alignment method. Furthermore, we use the dataset to train a state-of-the-art neural machine translation model, and compare it to a model trained on a general simplification dataset using an automatic evaluation, and an extensive human-expert evaluation.
TeLEx: learning signal temporal logic from positive examples using tightness metric

We propose a novel passive learning approach, TeLex, to infer signal temporal logic (STL) formulas that characterize the behavior of a dynamical system using only observed signal traces of the system. First, we present a template-driven learning approach that requires two inputs: a set of observed traces and a template STL formula. The unknown parameters in the template can include time-bounds of the temporal operators, as well as the thresholds in the inequality predicates. TeLEx finds the value of the unknown parameters such that the synthesized STL property is satisfied by all the provided traces and it is tight. This requirement of tightness is essential to generating interesting properties when only positive examples are provided and there is no option to actively query the dynamical system to discover the boundaries of legal behavior. We propose a novel quantitative semantics for satisfaction of STL properties which enables TeLEx to learn tight STL properties without multidimensional optimization. The proposed new metric is also smooth. This is critical to enable the use of gradient-based numerical optimization engines and it produces a 30x to 100x speed-up with respect to the state-of-art gradient-free optimization. Second, we present a novel technique for automatically learning the structure of the STL formula by incrementally constructing more complex formula guided by the robustness metric of subformula. We demonstrate the effectiveness of the overall approach for learning STL formulas from only positive examples on a set of synthetic and real-world benchmarks.
Human Gaze-Driven Spatial Tasking of an Autonomous MAV

In this letter, we address the problem of providing human-assisted quadrotor navigation using a set of eye tracking glasses. The advent of these devices (i.e., eye tracking glasses, virtual reality tools, etc.) provides the opportunity to create new, noninvasive forms of interaction between humans and robots. We show how a set of glasses equipped with gaze tracker, a camera, and an inertial measurement unit (IMU) can be used to estimate the relative position of the human with respect to a quadrotor, and decouple the gaze direction from the head orientation, which allows the human to spatially task (i.e., send new 3-D navigation waypoints to) the robot in an uninstrumented environment. We decouple the gaze direction from head motion by tracking the human's head orientation using a combination of camera and IMU data. In order to detect the flying robot, we train and use a deep neural network. We experimentally evaluate the proposed approach, and show that our pipeline has the potential to enable gaze-driven autonomy for spatial tasking. The proposed approach can be employed in multiple scenarios including inspection and first response, as well as by people with disabilities that affect their mobility.
An Effective Subsuperpixel-Based Approach for Background Subtraction

How to achieve competitive accuracy and less computation time simultaneously for background estimation is still an intractable task. In this paper, an effective background subtraction approach for video sequences is proposed based on a subsuperpixel model. In our algorithm, the superpixels of the first frame are constructed using a simple linear iterative clustering method. After transforming the frame from a color format to gray level, the initial superpixels are divided into K smaller units, i.e., subsuperpixels, via the k-means clustering algorithm. The background model is then initialized by representing each subsuperpixel as a multidimensional feature vector. For the subsequent frames, moving objects are detected by the subsuperpixel representation and a weighting measure. In order to deal with ghost artifacts, a background model updating strategy is devised, based on the number of pixels represented by each cluster center. As each superpixel is refined via the subsuperpixel representation, the proposed method is more efficient and achieves a competitive accuracy for background subtraction. Experimental results demonstrate the effectiveness of the proposed method.
Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

The superior interpretability and uncertainty modeling ability of Takagi-Sugeno-Kang fuzzy system (TSK FS) make it possible to describe complex nonlinear systems intuitively and efficiently. However, classical TSK FS usually adopts the whole feature space of the data for model construction, which can result in lengthy rules for high-dimensional data and lead to degeneration in interpretability. Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weakens the clarity and interpretability of TSK FS. To address these issues, a concise zero-order TSK FS construction method, called ESSC-SL-CTSK-FS, is proposed in this paper by integrating the techniques of enhanced soft subspace clustering (ESSC) and sparse learning (SL). In this method, ESSC is used to generate the antecedents and various sparse subspace for different fuzzy rules, whereas SL is used to optimize the consequent parameters of the fuzzy rules, based on which the number of fuzzy rules can be effectively reduced. Finally, the proposed ESSC-SL-CTSK-FS method is used to construct con-cise zero-order TSK FS that can explain the scenes in high-dimensional data modeling more clearly and easily. Experiments are conducted on various real-world datasets to confirm the advantages.
Structure-Aware Deep Learning for Product Image Classification

Automatic product image classification is a task of crucial importance with respect to the management of online retailers. Motivated by recent advancements of deep Convolutional Neural Networks (CNN) on image classification, in this work we revisit the problem in the context of product images with the existence of a predefined categorical hierarchy and attributes, aiming to leverage the hierarchy and attributes to improve classification accuracy. With these structure-aware clues, we argue that more advanced deep models could be developed beyond the flat one-versus-all classification performed by conventional CNNs. To this end, novel efforts of this work include a salient-sensitive CNN that gazes into the product foreground by inserting a dedicated spatial attention module; a multiclass regression-based refinement that is expected to predict more accurately by merging prediction scores from multiple preceding CNNs, each corresponding to a distinct classifier in the hierarchy; and a multitask deep learning architecture that effectively explores correlations among categories and attributes for categorical label prediction. Experimental results on nearly 1 million real-world product images basically validate the effectiveness of the proposed efforts individually and jointly, from which performance gains are observed.
Stance taking with ‘laugh’ particles and emojis – Sequential and functional patterns of ‘laughter’ in a corpus of German WhatsApp chats

The present study investigates functions of ‘laugh’ particles like haha, hehe or hihi in a corpus of German WhatsApp text messages. It contributes to research on humor, ‘laugh’ particles and emojis in mobile messaging chats by examining the different stances the interlocutors take when they use ‘laugh’ particles in WhatsApp postings and the role emojis play in these postings. Based on an interactional approach to the analysis of WhatsApp chats, the study shows that ‘laugh’ particles are prototypically deployed in a posting-initial position and that they relate to previous postings or utterances ‘Laugh’ particles can be used to establish or support a humorous joking modality (laughing with). Particularly in group chats users can cooperatively turn one participant into the target of their ‘laughter’ (laughing at). ‘Laugh’ particles and emojis are closely connected, as emojis help to contextualize these different ‘laughter’ stances.
Privacy-preserving task recommendation with win-win incentives for mobile crowdsourcing

Mobile crowdsourcing enables mobile requesters to publish tasks, which can be accomplished by workers with awards. However, existing task allocation schemes face tradeoff between effectiveness and privacy preservation, and most of them lack consideration of win-win incentives for both requesters and workers participation. In this paper, we propose a privacy-preserving task recommendation scheme with win-win incentives in crowdsourcing through developing advanced attribute-based encryption with preparation/online encryption and outsourced decryption technologies. Specifically, we design bipartite matching between published tasks and participant workers, to recommend tasks for eligible workers with interests and provide valuable task accomplishment for requesters in a win-win manner. Furthermore, our scheme reduces encryption cost for requesters by splitting encryption into preparation and online phases, as well as shifts most of the decryption overhead from the worker side to the service platform. Privacy analysis demonstrates requester and worker privacy preservation under chosen-keyword attack and chosen-plaintext attack. Performance evaluation shows cost-efficient computation overhead for requesters and workers.
A big data driven distributed density based hesitant fuzzy clustering using Apache spark with application to gene expression microarray

Abstract This paper introduces a distributed density based clustering approach that benefits from the hesitant fuzzy weighted correlation coefficient as its similarity measure. All the proposed clustering steps adapt completely with the distributed Spark computation framework and the data dependency reaches its least possible using the density reachability concept. By proposing a Resilient Distributed Dataset (RDD) localized subclustering method, disk I/O burden of the MapReduce based clustering approaches has been solved as well. The comparison of the clustering results with similar works shows the superiority of the proposed algorithm in precision and cluster validity indexes. The proposed method also shows relative robustness to the presence of noise in comparison with similar recent works. The proposed approach shows better precision and validity index in comparison with MapReduce base algorithms while outperforming MapReduce based approaches in computational burden. The scalability of the proposed method is almost similar to the standard Spark machine learning library.
Eye-tracker algorithms to detect saccades during static and dynamic tasks: a structured review

Eye-tracking devices have become widely used as clinical assessment tools in a variety of applied-scientific fields to measure saccadic eye movements. With the emergence of multiple static and dynamic devices, the concurrent need for algorithm development and validation is paramount.This review assesses the prevalence of current saccade detection algorithms, their associated validation methodologies and the suitability of their application. Medline, Embase, PsychInfo, Scopus, IEEEXplore and ACM Digital Library databases were searched. Two independent reviewers and an adjudicator screened articles describing the detection of saccades from raw infrared/video-based eye-tracker data.Thirteen articles were screened and met the inclusion criteria. Overall, the majority of reviewed saccadic detection algorithms used simple velocity-based classifications with static eye-tracking systems. Studies demonstrated validity but are limited by the static nature of testing. Heterogeneity in system design, proprietary and bespoke algorithmic methods used, processing strategies, and outcome reporting is evident.This paper suggests the use of a more standardised methodology to facilitate experimental validity and improve comparison of results across studies.
Online romance scams and victimhood

Online romance scams defraud dating website users of large amounts of money and inflict serious psychological harm. Victims of these scams often blame themselves for their losses and are blamed by others. We consider whether victims actually do share responsibility with the scammer for their losses. Three sorts of cases are particularly relevant: (i) where there are relatively many abortive meetings and even more fruitless money transfers in a single scam; (ii) where someone is a repeat scam victim; and (iii) where the victim has been warned by authorities that they are currently a victim of a scam and pay anyway. We argue that responsibility sometimes is shared, but that losses can be out of proportion to imprudence. Scam victims sometimes violate epistemic norms, but in ways that are peculiar to romantic attachment. The paper combines the methods of qualitative psychological research on scam victims and analytic philosophy (Research for this paper was supported by Grant EP/N028112/1 from the UK Engineering and Physical Sciences Research Council).
ADvoCATE: A Consent Management Platform for Personal Data Processing in the IoT Using Blockchain Technology

The value of personal data generated and managed by smart devices which comprise the Internet of Things (IoT) is unquestionable. The EU General Data Protection Regulation (GDPR) that has been recently put in force, sets the cornerstones regarding the collection and processing of personal data, for the benefit of Data Subjects and Controllers. However, applying this regulation to the IoT ecosystem is not a trivial task. This paper proposes ADvoCATE, a user-centric solution that allows data subjects to easily control consents regarding access to their personal data in the IoT ecosystem and exercise their rights defined by GDPR. It also assists Data Controllers and Processors to meet GDPR requirements. A blockchain infrastructure ensures the integrity of personal data processing consents, while the quality thereof is evaluated by an intelligence service. Finally, we present some preliminary details of a partial implementation of the proposed framework.
P-CNN: Enhancing text matching with positional convolutional neural network

In recent years, positional information has shown good performance in deep neural networks for text matching. Most positional deep neural networks focus on modeling positional information based on the word-level matching signals, whereas the positional influence and interaction among texts have not been well studied during the generation of matching scores. In this paper, we propose a novel positional convolution neural matching model that holds positional influence and interaction in multiple perspectives for text matching. To be specific, we first encode the perspectives of positional information at the word level, the phrase level, and the sentence level. Then, a position-similarity mapping layer is defined to project word-level positional information to local matching signals, which bridges the gap between the word embedding input layer and the hidden convolutional layer. After that, a position-sensible convolution filter is proposed to capture and extract positional information at the phrase level and the sentence level. In particular, we assume that a phrase or sentence has an influence on its neighboring phrase or sentence, and the position-sensible convolution filter is generated on the basis of influence propagation, instead of a random matrix, as in the traditional convolutional neural network. Finally, we offer a multiple-perspective matching function to aggregate positional information at the word level, phrase level, and sentence level. Three standard datasets are used to evaluate our approach, namely ClueWeb-09-Cat-B for web search and TREC-QA and WikiQA for answer selection. It is notable that we achieve the new state-of-the-art performance on ClueWeb-09-Cat-B. Furthermore, on TREC-QA and WikiQA, our model outperforms all deep neural network approaches without an attention mechanism, and is comparable to if not better than approaches that rely on attention mechanisms.
Accurate Vehicle Counting Approach Based on Deep Neural Networks

Vehicle counting is considered one of the most important applications in traffic control and management. To count vehicles, synchronous vehicle detection and tracking should be carried out. Recently, detection via deep neural networks (DNN) has achieved good performance. However, exploiting the DNN efficiently for vehicle counting is still challenging. In this paper, an efficient approach for vehicle counting employing DNN and KLT tracker is proposed. To decrease the time complexity, vehicles are detected via DNN every N-frames, N=15 for example. Trajectories are extracted by tracking corner points through the N-frames. Then an efficient algorithm is introduced to assign unique vehicle labels to their corresponding trajectories. The proposed results, performed on diverse vehicle videos, show that vehicles are accurately tracked and counted whatever they are detected one or more times by the DNN.