Reviewerly/exHarmony huggingface dataset:
A2Text-Net: A Novel Deep Neural Network for Sarcasm Detection

Sarcasm is a common form of irony in which users usually express their negative attitudes using contrary words. Predicting sarcasm is an essential part of investigating human social interaction. Improvements in classifying sarcasm have the potential to improve other dimensions of human sentiment (e.g., brand preference, political views). In face-to-face communication, the changing of voice, eye contact, physical position, etc. provides the audience with cues to detect sarcasm. However, detecting sarcasm exclusively with text is particularly challenging, given the lack of these subtle human-centric cues. In this study, we employed a new deep neural network: A2Text-Net to mimic the face-to-face speech, which integrates auxiliary variables such as punctuations, part of speech (POS), numeral, emoji, etc. to increase classification performance. The experiment results provide evidence that our A2Text-Net approach improves classification performance over conventional machine learning and deep learning algorithms.
Ontology-driven representation of knowledge for geological maps

This paper presents an ontology-driven representation of knowledge for geological maps. The ontological formal language allows for a machine-readable encoding of the Earth scientist's interpretation through semantic categories and properties and is credited to support knowledge sharing and interoperability. We introduce an ontology-driven method for the interpretation and the encoding of the map data that employs shared vocabularies and resources encoded through ontologies in order to prevent the use of ambiguous terms. The approach relies on a computational ontology of the geological knowledge (OntoGeonous), which formalizes a number of geological knowledge sources (including GeoScienceML), to guide the interpretation process. The design of the database underlying the map (OntoGeoBase) constrains the process of data entry to refer to the terminology conveyed by the taxonomic-axiomatic nature of the ontology. This reduces the amount of implicit knowledge favouring a conceptual alignment of the ancillary documentation with the map, leading to a better comprehension of map and allowing the traceability of the interpretation.
An Automatic Background Filtering Method for Detection of Road Users in Heavy Traffics Using Roadside 3-D LiDAR Sensors With Noises

Background filtering for roadside Light Detecting and Ranging (LiDAR) data could benefit detection of road users with high frequency and accuracy. But existing methods are unsatisfactory under traffic congestions and neglect noises of LiDAR sensors. This paper attempts to develop background identification and filtering methods for heavy traffics and LiDAR noises. In particular, a 3-D space-time diagram of the LiDAR's detection space over frames is divided into plenty of subspace-frames, where one subspace-frame refers to one subspace from the isometric division of detection space in one frame. For subspace-frames at the same position, an unsupervised clustering method with unknown number of clusters is applied for classifying subspace-frames into different groups and the background group is identified based on background characteristics. The proposed clustering method is a t-distribution mixture model with constraints, where weights follow a Dirichlet distribution so that some weights have the possibility to be 0. A Bayesian estimation framework is developed to carry out the clustering. Then each background subspace-frame is divided into cubes with equal lengths. By aggregating points over frames in the same cube, the cube with aggregated points is regarded as background cube. For background filtering, points in background cubes are background points and excluded. Finally, examined by data from a Velodyne VLP-32 LiDAR sensor, the proposed approach has better filtering results no matter under free-flow conditions or congestions. Using background filtering results for road users' detection is introduced as well.
Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags

Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user’s body. Acceleration sum vector magnitude (SVM), angular velocity SVM, and vertical angle, calculated using inertial data captured from an inertial measurement unit were used to develop the algorithm. To calculate the vertical angle accurately, a complementary filter with a proportional integral controller was used to minimize integration errors and the effect of external impacts. In total, 30 healthy young men were recruited to simulate 6 types of falls and 14 activities of daily life. The developed algorithm achieved 100% sensitivity, 97.54% specificity, 98.33% accuracy, and an average lead time (i.e., the time between the fall detection and the collision) of 280.25 ± 10.29 ms with our experimental data, whereas it achieved 96.1% sensitivity, 90.5% specificity, and 92.4% accuracy with the SisFall public dataset. This paper demonstrates that the algorithm achieved a high accuracy using our experimental data, which included some highly dynamic motions that had not been tested previously.
Challenges and Opportunities for Publishing IIoT Data in Manufacturing as a Service Business

Cloud manufacturing (CMfg) can manage mass manufacturing (MM) resources and capabilities, and provide them as services via the Internet. Undoubtedly, multiple manufacturing clouds will have abundant services in terms of function, price, reliability, location, etc. The implementation of such concept in a wider structure than a single MC requires to provide explicit data about production sequence, machine allocation, production parameters, quality of service (QoS), and several other key performance indicators (KPIs). However, the state of the art approach is based on private access to such data, under specific contract requirements, and it becomes a limitation to the evolution of the concept itself. In particular it becomes limiting when the CMfgs involve multiple process owners distributed throughout a complex value creating network, such as production, logistics, quality control, but also, third parties like auditor interested in energy consumption, or machine maintenance provider, also interested in monitoring the effective usage of the machines, etc. This paper will analyze deeply the structural and functional characteristics of such CMfgs and then, it will propose a business intelligence architecture that aims to enable publishing relevant KPIs related to interested process data, with the convenient layer of trustworthiness. Such public access will enable the assessment to different agents at different time frequency and, finally, with different motivation, thus bringing practical information about time delays for the data availability and scalability factors.
A Comparative Analysis of Traditional and Deep Learning-Based Anomaly Detection Methods for Streaming Data

With the Internet of Things (IoT) devices becoming an integral part of human life, the need for robust anomaly detection in streaming data has also been elevated. Dozens of distance-based, density-based, kernel-based, and cluster-based algorithms have been proposed in the area of anomaly detection. Recently, because of the robustness of the deep neural networks (DNN), different deep learning-based anomaly detection methods have also been proposed. With all these rapid developments, there exists a small number of comparative studies for anomaly detection methods. Even in those studies, the comparison is done only in typical anomaly detection settings without taking the streaming data into consideration. The presence of intrinsic time-series characteristics like trend, seasonality, and change-point makes it important to study the behavior of commonly used anomaly detection methods on streaming data. Moreover, the comparison of traditional methods with deep learning-based methods also brings exciting insights about the data which are generally overlooked by traditional methods. In this study, we compare 13 anomaly detection methods on two commonly used streaming data sets. We used four different evaluation metrics to evaluate the methods from different perspectives. Our analysis reveals that the deep learning-based anomaly detection methods are superior to traditional anomaly detection methods.
Artificial Intelligence in Conversational Agents

Artificial intelligence (AI) is gaining traction in service-oriented businesses in the form of chatbots. A chatbot is a popular type of social AI that uses natural language processing to communicate with users. Past studies have shown discrepancies in terms of whether or not a chatbot should communicate and behave like a human. This article aims to explore these discrepancies in order to provide a theoretical contribution of a list of factors related to perceived humanness in chatbots and how these may consequently lead to a positive user experience. The results suggest that a chatbot should have the following characteristics: avoiding small talk and maintaining a formal tone; identifying itself as a bot and asking how it can help; providing specific information and articulating itself with sophisticated choices of words and well-constructed sentences; asking follow-up questions during decision-making processes and; providing an apology when the context is not comprehensible, followed by a question or a statement to dynamically move a conversation forward. These results may have implications for designers working in the field of AI as well as for the wider debates and the broader discourse around the adoption of AI in society.
Online Federated Multitask Learning

With the popular use of mobile devices, it becomes increasingly important to conduct analysis on distributed data collected from multiple devices. Federated learning is a distributed learning framework which takes advantage of the training data and computational ability of scattered mobile devices to learn prediction models, and multi-task learning infers personalized but shared models among devices. Some recent work has integrated federated and multi-task learning, but such approaches may be impractical and inefficient in the online scenario, e.g., when new mobile devices keep joining the mobile computing system. To address this challenge, we propose OFMTL, an online federated multi-task learning algorithm, which learns the model parameters for the new device without revisiting the data of existing devices. The model parameters are derived by an effective way that combines the information inferred from local data and information borrowed from existing models. Through extensive experiments on three real datasets, we show that the proposed OFMTL framework achieves comparable accuracy to the existing algorithms but with much smaller computation, transmission and storage cost.
Privacy and utility preserving sensor-data transformations

Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications. To mitigate these threats, we propose mechanisms to transform sensor data before sharing them with applications running on users’ devices. These transformations aim at eliminating patterns that can be used for user re-identification or for inferring potentially sensitive activities, while introducing a minor utility loss for the target application (or task). We show that, on gesture and activity recognition tasks, we can prevent inference of potentially sensitive activities while keeping the reduction in recognition accuracy of non-sensitive activities to less than 5 percentage points. We also show that we can reduce the accuracy of user re-identification and of the potential inference of gender to the level of a random guess, while keeping the accuracy of activity recognition comparable to that obtained on the original data.
Answering Questions about Data Visualizations using Efficient Bimodal Fusion

Chart question answering (CQA) is a newly proposed visual question answering (VQA) task where an algorithm must answer questions about data visualizations, e.g. bar charts, pie charts, and line graphs. CQA requires capabilities that natural-image VQA algorithms lack: fine-grained measurements, optical character recognition, and handling out-of-vocabulary words in both questions and answers. Without modifications, state-of-the-art VQA algorithms perform poorly on this task. Here, we propose a novel CQA algorithm called parallel recurrent fusion of image and language (PReFIL). PReFIL first learns bimodal embeddings by fusing question and image features and then intelligently aggregates these learned embeddings to answer the given question. Despite its simplicity, PReFIL greatly surpasses state-of-the art systems and human baselines on both the FigureQA and DVQA datasets. Additionally, we demonstrate that PReFIL can be used to reconstruct tables by asking a series of questions about a chart.
Adjustment of the Angstrom-Prescott equation from Campbell-Stokes and Kipp-Zonen sunshine measures at different timescales in Spain

The sunshine-based Ångström–Prescott model and the temperature-based is widely used in the scientific literature, and, in general, it is accepted that the sunshine-based methods are more accurate. Sunshine duration is registered in many climatological stations, and it is highly correlated with solar radiation. However, in many applications of the Ångström–Prescott equation, no attention is payed to the sensor used for recording sunshine duration. In Spain, the Campbell-Stokes sensors are being replaced by Kipp and Zonen sensors. This study assessed the Ångström–Prescott goodness-of-fit and adjusted coefficients for Campbell-Stokes and Kipp and Zonen daily records at 10 stations in Spain. The analysis considered three different temporary windows, namely global, seasonal and monthly scales based. The results show differences in the sunshine hours measured by the two instruments, mainly because the Campbell-Stokes recorder is prone to over-burning. The Kipp and Zonen sensors can be considered as suitable as the Campbell-Stokes sensor, and provides a significant reduction in the statistical errors. The results also highlight the importance calibrating the Ångström–Prescott equation using seasonal and monthly coefficients, instead of using a unique pair of values. Further, the equation performance shows a clear seasonal pattern, with a higher accuracy during the summer.
Rotation-invariant Mixed Graphical Model Network for 2D Hand Pose Estimation

In this paper, we propose a new architecture named Rotation-invariant Mixed Graphical Model Network (R-MGMN) to solve the problem of 2D hand pose estimation from a monocular RGB image. By integrating a rotation net, the R-MGMN is invariant to rotations of the hand in the image. It also has a pool of graphical models, from which a combination of graphical models could be selected, conditioning on the input image. Belief propagation is performed on each graphical model separately, generating a set of marginal distributions, which are taken as the confidence maps of hand keypoint positions. Final confidence maps are obtained by aggregating these confidence maps together. We evaluate the R-MGMN on two public hand pose datasets. Experiment results show our model outperforms the state-of-the-art algorithm which is widely used in 2D hand pose estimation by a noticeable margin.
Augmented Reality and Functional Skills Acquisition Among Individuals With Special Needs: A Meta-Analysis of Group Design Studies

Augmented reality (AR) has the potential to support individuals with special needs and to enable their development of daily living skills. This meta-analysis study examined the effect of AR on functional skills acquisition across individuals affected by different disabilities. Group design studies based on a random-effects model alongside the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were used in this study. A total of 119 individuals with different types of disabilities (including intellectual disability, autism spectrum disorder, attention deficit hyperactivity disorder, Down syndrome, hearing disability, and visual disability) were obtained from seven studies. The overall effect size of AR across the seven studies was significant. The results showed that AR can be effective and helpful for individuals with disabilities to help them make daily decisions and guide their actions in society. The implications for practice and research as well as the possible areas that require further investigation are discussed.
Using Ethereum Blockchain for Distributed Attribute-Based Access Control in the Internet of Things

Access control has been recognized as a critical issue for preventing unauthorized access to the resources in Internet of Things (IoT) systems. This paper proposes an Attribute-Based Access Control (ABAC) framework for IoT systems by using the emerging Ethereum smart contract technology. The framework consists of one Policy Management Contract (PMC), one Subject Attribute Management Contract (SAMC), one Object Attribute Management Contract (OAMC) and one Access Control Contract (ACC). The PMC, SAMC and OAMC are responsible for storing and managing the ABAC policies, the attributes of subjects (i.e., entities accessing resources) and the attributes of objects (i.e., resources being accessed), respectively. When receiving access requests, the ACC retrieves the subject attributes and object attributes as well as the corresponding policy from the SAMC, OAMC and PMC to perform the access control. Combining the ABAC model and the blockchain technology, this framework is expected to achieve distributed, trustworthy and fine-grained access control for IoT systems. To show the feasibility of the proposed framework, we construct a local private Ethereum blockchain system to implement the four smart contracts and also conduct experiments to test the monetary and time cost.
Head and Body Motion Prediction to Enable Mobile VR Experiences with Low Latency

As virtual reality (VR) applications become popular, the desire to enable high-quality, lightweight and mobile VR leads to various edge/cloud-based techniques. This paper introduces a predictive pre-rendering approach to address the ultra-low latency challenge in edge/cloud-based six Degrees of Freedom (6DoF) VR. Compared to 360-degree videos and 3DoF (head motion only) VR, 6DoF VR supports both head and body motions, thus not only viewing direction, but also viewing position changes. In our approach, the predictive view is rendered in advance based on the predicted viewing direction and position, leading to a reduction in latency. The key to achieving this efficient predictive pre-rendering approach is to predict the head and body motion accurately using past head and body motion traces. We develop a deep learning-based model and validate its ability using a dataset of over 840,000 samples for head and body motion.
Tactical interventions in online hate speech: The case of #stopIslam

This article sets out findings from a project focused on #stopIslam, a hashtag that gained prominence following the Brussels terror attack of 2016. We initially outline a big data analysis which shows how counter-narratives – criticizing #stopIslam – momentarily subverted negative news reporting of Muslims. The rest of the article details qualitative findings that complicate this initial positive picture. We set out key tactics engaged in by right-wing actors, self-identified Muslim users, would-be allies and celebrities and elucidate how these tactics were instrumental in the direction, dynamics and legacies of the hashtag. We argue that the tactical interventions of tightly bound networks of right-wing actors, as well as the structural constraints of the platform, not only undermined the longevity and coherence of the counter-narratives but subtly modulated the affordances of Twitter in ways that enabled these users to extend their voice outwards, reinforcing long-standing representational inequalities in the process.
Step on the Gas? A Better Approach for Recommending the Ethereum Gas Price

In the Ethereum network, miners are incentivized to include transactions in a block depending on the gas price specified by the sender. The sender of a transaction therefore faces a trade-off between timely inclusion and cost of his transaction. Existing recommendation mechanisms aggregate recent gas price data on a per-block basis to suggest a gas price. We perform an empirical analysis of historic block data to motivate the use of a predictive model for gas price recommendation. Subsequently, we propose a novel mechanism that combines a deep-learning based price forecasting model as well as an algorithm parameterized by a user-specific urgency value to recommend gas prices. In a comprehensive evaluation on real-world data, we show that our approach results on average in costs savings of more than 50% while only incurring an inclusion delay of 1.3 blocks, when compared to the gas price recommendation mechanism of the most widely used Ethereum client.
Free choice, simplification, and Innocent Inclusion

We propose a modification of the exhaustivity operator from Fox (in: Sauerland and Stateva (eds) Presupposition and implicature in compositional semantics, Palgrave Macmillan, London, pp 71–120, 2007. https://doi.org/10.1057/9780230210752_4 ) that on top of negating all the Innocently Excludable alternatives affirms all the ‘Innocently Includable’ ones. The main result of supplementing the notion of Innocent Exclusion with that of Innocent Inclusion is that it allows the exhaustivity operator to identify cells in the partition induced by the set of alternatives (assign a truth value to every alternative) whenever possible. We argue for this property of ‘cell identification’ based on the simplification of disjunctive antecedents and the effects on free choice that arise as the result of the introduction of universal quantifiers. We further argue for our proposal based on the interaction of only with free choice disjunction.
EyeNet: Attention Based Convolutional Encoder-Decoder Network for Eye Region Segmentation

With the immersive development in the field of augmented and virtual reality, accurate and speedy eye-tracking is required. Facebook Research has organized a challenge, named OpenEDS Semantic Segmentation challenge for per-pixel segmentation of the key eye regions: the sclera, the iris, the pupil, and everything else (background). There are two constraints set for the participants viz MIOU and the computational complexity of the model. More recently, researchers have achieved quite a good result using the convolutional neural networks (CNN) in segmenting eye-regions. However, the environmental challenges involved in this task such as low resolution, blur, unusual glint and, illumination, off-angles, off-axis, use of glasses and different color of iris region hinder the accuracy of segmentation. To address the challenges in eye segmentation, the present work proposes a robust and computationally efficient attention-based convolutional encoder-decoder network for segmenting all the eye regions. Our model, named EyeNet, includes modified residual units as the backbone, two types of attention blocks and multi-scale supervision for segmenting the aforesaid four eye regions. Our proposed model achieved a total score of 0.974(EDS Evaluation metric) on test data, which demonstrates superior results compared to the baseline methods.