Reviewerly/exHarmony huggingface dataset:
Empower event detection with bi-directional neural language model

Event detection is an essential and challenging task in Information Extraction (IE). Recent advances in neural networks make it possible to build reliable models without complicated feature engineering. However, data scarcity hinders their further performance. Moreover, training data has been underused since majority of labels in datasets are not event triggers and contribute very little to the training process. In this paper, we propose a novel multi-task learning framework to extract more general patterns from raw data and make better use of the training data. Specifically, we present two paradigms to incorporate neural language model into event detection model on both word and character levels: (1) we use the features extracted by language model as an additional input to event detection model. (2) We use a hard parameter sharing approach between language model and event detection model. The extensive experiments demonstrate the benefits of the proposed multi-task learning framework for event detection. Compared to the previous methods, our method does not rely on any additional supervision but still beats the majority of them and achieves a competitive performance on the ACE 2005 benchmark.
3D shape shearography technique for surface strain measurement of free-form objects

Shearography is a non-destructive testing technique that provides full-field surface strain characterization. Previous inspection of flat objects or simple geometric shapes has been reported. However, real-life objects especially in aerospace, transport, or cultural heritage are not flat, but their inspection with shearography is of interest for both hidden defect detection and material characterization. Accurate strain measurement of a highly curved or free-form surface needs to be performed by combining in-line object shape measuring and processing of the shearography data in 3D. Previous research has not provided a general solution for that. This paper presents a new approach of 3D shape shearography which is based on the integration of a structured light projector for in-line shape measuring with 3D shearography. For the experimental part, a 3D shape shearography system prototype was developed and its performance was evaluated with a cylinder specimen loaded by internal pressure and compared with strain gauges.
Turning video into traffic data – an application to urban intersection analysis using transfer learning

With modern socio-economic development, the number of vehicles in metropolitan cities is growing rapidly. Therefore, obtaining real-time traffic volume estimates has a very important significance in using the limited road space and traffic infrastructure. In this study, the authors present a video-based traffic volume and direction estimation at road intersections. To discriminate the vehicles from the remaining foreground objects, vehicle recognition is performed by training a deep-learning architecture from a pre-trained model. This method, called transfer learning, primarily circumvents the requirement of huge labelled datasets and the time for training the network. The video sequence is first detected for moving foreground regions or patches. The trained model is subsequently used to classify the vehicles. The vehicles are tracked, and trajectory patterns are clustered using standard techniques. The number and direction of vehicles are noted, which are later compared with the manually observed values. All experiments were performed on real-life surveillance sequences recorded at four different traffic intersections in the city of Kolkata.
Using a game engine for simulation in ergonomics analysis, design and education: An exploratory study

Among the possible approaches for building virtual environments (VE), researchers have recently started employing game engines (GE). Although there are already studies reporting the usage of GE-based VEs, their potential for supporting a more comprehensive workspace analysis (considering the physical, organizational and cognitive aspects of work) has yet to be better understood. The main goal of this paper is to investigate how a GE-based simulation of a real workplace (a local control room in an oil refinery) can be used as a tool by practitioners and researchers in evaluating work conditions. Participants (n = 38) were recruited to explore the simulation and evaluate the workplace dimensions represented. A comparison between the scores participants attributed to the work dimensions and the scores assigned by the ergonomics consultant was performed through a statistical test to verify whether they significantly differed or not. Out of the 10 aspects evaluated, only 3 presented significant differences, thus showing that GE suitability for ergonomics analysis is conditioned to the aspects represented. Qualitative data analysis highlighted participants' perception of GEs potential as an analysis and educational tool, as well as a medium for fostering communication and stakeholder involvement in the design process.
Clustering of multi-view relational data based on particle swarm optimization

Clustering of multi-view data has received increasing attention since it explores multiple views of data sets aiming at improving clustering accuracy. Particle Swarm Optimization (PSO) is a well-known population-based meta-heuristic successfully used in cluster analysis. This paper introduces two hybrid clustering methods for multi-view relational data. These hybrid methods combine PSO and hard clustering algorithms based on multiple dissimilarity matrices. These methods take advantage of the global convergence ability of PSO and the local exploitation of hard clustering algorithms in the position update step, aiming to improve the balance between exploitation and exploration processes. Moreover, the paper provides adapted versions of 11 fitness functions suitable for vector data aiming at dealing with multi-view relational data. Two performance criteria were used to evaluate the clustering quality using the two proposed methods over eleven real-world data sets including image and document data sets. Among new findings, it was observed that the top three fitness functions are Silhouette index, Xu index and Intra-cluster homogeneity. The performance of the proposed algorithms was compared with previous single and multi-view relational clustering algorithms. The results show that the proposed methods significantly outperformed the other algorithms in the majority of cases. The results reinforce the importance of the application of techniques such as PSO-based clustering algorithms in the field of expert systems and machine learning. Such application enhances classification accuracy and cluster compactness. Besides, the proposed algorithms can be useful tools in content-based image retrieval systems, providing good categorizations and automatically learning relevance weights for each cluster of images and sets of views.
An efficient neurodynamic model to solve nonlinear programming problems with fuzzy parameters

In the present research, we obtain the solution of the fuzzy nonlinear programming problems (FNLPs) using recurrent neural network models. Since there is a few study and research for resolving of FNLP by recurrent neural network models, we obtain a new strategy to solve the problem. Reformulating the original problem to an interval program and then weighting problem, the Karush–Kuhn–Tucker (KKT) optimality conditions are presented. Moreover, we apply the KKT conditions into a recurrent neural network model as a high-performance tool to solve the problem. Besides, the global convergence and the Lyapunov stability of the system are proved in this research. In the final step, several illustrative examples are given to substantiate the obtained results. Reported results are compared with other published papers.
ParlAmI: A Multimodal Approach for Programming Intelligent Environments

The proliferation of Internet of Things (IoT) devices and services and their integration in intelligent environments creates the need for a simple yet effective way of controlling and communicating with them. Towards such a direction, this work presents ParlAmI, a conversational framework featuring a multimodal chatbot that permits users to create simple “if-then” rules to define the behavior of an intelligent environment. ParlAmI delivers a disembodied conversational agent in the form of a messaging application named MAI, and an embodied conversational agent named nAoMI employing the programmable humanoid robot NAO. This paper describes the requirements and architecture of ParlAmI, the infrastructure of the “Intelligent Home” in which ParlAmI is deployed, the characteristics and functionality of both MAI and nAoMI, and finally presents the findings of a user experience evaluation that was conducted with the participation of sixteen users.
Onboard Decision-Making for Nominal and Contingency sUAS Flight

This study presents an onboard decision-making architecture for small unmanned aerial systems (sUAS). The decision-maker is part of NASA's SAFE50 project that is working under the UAS Traffic Management (UTM) Technical Capability Level (TCL) 4 to provide autonomous point-to-point UAV flight in BVLOS, high-density urban environments. The decision-maker monitors various metrics to determine the safety and feasibility of the mission and categorizes flight states as Nominal, Off-Nominal, Alternate Land, and Land Now in a finite state machine. Changes in the monitored metrics serve as transitions in the state machine and trigger replanning. Navigation degradation and communication failure are simulated to show the feasibility of the decision-maker framework in appropriately switching the flight state.
An Extension to the <i>Noisy-OR</i> Function to Resolve the ‘Explaining Away’ Deficiency for Practical Bayesian Network Problems

The “leaky noisy-OR” function is a common and popular method used to simplify the elicitation of complex conditional probability tables in Bayesian networks involving Boolean variables. It has proven to be useful for approximating the required relationship in many real-world situations where there is a set of two or more variables that are potential causes of a single effect variable. However, one of the properties of leaky noisy-OR is Conditional Inter-causal Independence (CII). This property means that the `explaining away' behavior-one of the most powerful benefits of BN inference-is not present when the effect variable is observed as false. Yet, for many real-world problems where the leaky noisy-OR has been considered, this behavior would be expected, meaning that leaky noisy-OR is deficient as an approximation of the required relationship in such cases. There have been previous attempts to adapt the noisy-OR to resolve this problem. However, they require too many additional parameters to be elicited. We describe a simple but powerful extension to leaky noisy-OR that requires only a single additional parameter. While it does not solve the CII problem in all cases, it does resolve most of the explaining away deficiencies that occur in practice. The problem and solution is illustrated using an example from intelligence analysis.
No-Reference quality assessment of noisy images with local features and visual saliency models

Image quality assessment (IQA) measures predict the perceived quality of evaluated images, aiming to replace time-consuming human evaluation. This is particularly important for the automatic comparison of image processing techniques which often modify image content. Since the presence of noise highly affects the perception of images and only a few IQA measures address this type of distortion, a new no-reference IQA measure is proposed in this paper. In the introduced measure, two aspects of the Human Visual System (HVS) are considered. The sensitivity of the HVS to local image distortions is expressed by similarities between descriptors obtained with Speeded-Up Robust Features (SURF) technique. In order to improve the quality prediction, image patches for description are extracted from a gradient map of an input image. Noise also affects the visual saliency, since it decreases attention caused by image content. In the approach, visual saliency models of image patches are obtained and evaluated using adapted Visual Saliency-based Index (VSI). Experimental evaluation on popular IQA benchmarks reveals that the proposed measure outperforms the related state-of-the-art techniques. The applicability of the proposed method to an automatic selection of the best denoising algorithm is also discussed in the paper.
Low-Resource Machine Transliteration Using Recurrent Neural Networks

Grapheme-to-phoneme models are key components in automatic speech recognition and text-to-speech systems. With low-resource language pairs that do not have available and well-developed pronunciation lexicons, grapheme-to-phoneme models are particularly useful. These models are based on initial alignments between grapheme source and phoneme target sequences. Inspired by sequence-to-sequence recurrent neural network--based translation methods, the current research presents an approach that applies an alignment representation for input sequences and pretrained source and target embeddings to overcome the transliteration problem for a low-resource languages pair. Evaluation and experiments involving French and Vietnamese showed that with only a small bilingual pronunciation dictionary available for training the transliteration models, promising results were obtained with a large increase in BLEU scores and a reduction in Translation Error Rate (TER) and Phoneme Error Rate (PER). Moreover, we compared our proposed neural network--based transliteration approach with a statistical one.
Potential Applications for Broadband Global Internet Connectivity

The Internet really is now part of our social fabric and is essential to how we connect, communicate, share, and collaborate. Digital technologies, information and communication technologies, and broadband global connectivity when combined offer various opportunities to improve people's lives in worldwide societies by exchanging and sharing information. Some of the potential applications of broadband global connectivity include social connections, health, well-being and telemedicine, education, business and financial transaction, emergency response, and search and rescue These all are possible with Internet access and are discussed.
A client-based adaptation framework for 360-degree video streaming

360-degree video is one of the key components of Virtual Reality (VR) applications. 360-degree videos viewed on Head Mounted Displays can offer impressive viewing experiences to users. Yet, streaming of 360-degree videos over the Internet is a very challenging task since it requires extremely high bandwidth. To reduce the bandwidth requirement while still providing good experiences, viewport adaptive streaming has been introduced. In this paper, we propose a client-based adaptation framework for viewport adaptive streaming of 360 videos, which can support different application scenarios. The key components and important issues are presented with a general problem formulation for tile version selection. Especially, we introduce for the first time the use of bitrate and quality estimation in viewport adaptive streaming of 360 videos. Experiments show that the proposed framework can significantly improve video quality for users. In addition, the impacts of buffering delay and projection formats in VR context are investigated.
Image Captioning With Visual-Semantic Double Attention

In this article, we propose a novel Visual-Semantic Double Attention (VSDA) model for image captioning. In our approach, VSDA consists of two parts: a modified visual attention model is used to extract sub-region image features, then a new SEmantic Attention (SEA) model is proposed to distill semantic features. Traditional attribute-based models always neglect the distinctive importance of each attribute word and fuse all of them into recurrent neural networks, resulting in abundant irrelevant semantic features. In contrast, at each timestep, our model selects the most relevant word that aligns with current context. In other words, the real power of VSDA lies in the ability of not only leveraging semantic features but also eliminating the influence of irrelevant attribute words to make the semantic guidance more precise. Furthermore, our approach solves the problem that visual attention models cannot boost generating non-visual words. Considering that visual and semantic features are complementary to each other, our model can leverage both of them to strengthen the generations of visual and non-visual words. Extensive experiments are conducted on famous datasets: MS COCO and Flickr30k. The results show that VSDA outperforms other methods and achieves promising performance.
EHNL: An efficient algorithm for mining high utility itemsets with negative utility value and length constraints

High utility itemsets (HUIs) mining is one of the emerging topics in frequent itemset mining (FIM). HUIs mining provides more informative and actionable information compared to FIM. Although many HUIs mining algorithms have been proposed in recent years. They incur the problem of generating a large number of candidate itemsets and most of the generated itemsets are tiny in size which degrade mining performance and action-ability. Apart from these problems, most of the algorithms work only with positive utility value. To overcome these issues, we propose an algorithm named EHNL (Efficient High utility itemsets mining with Negative utility and Length constraints). Although negative utility and constraint-based mining are commonly seen in real-world applications, mining HUIs with negative utility and length constraints has not yet been proposed in literature. Most of the traditional algorithms suffer from multiple dataset scanning problem. To reduce the scanning cost, we utilize dataset projection and transaction merging techniques. To enhance the performance of the proposed algorithm, we utilize sub-tree based pruning technique. To check the efficiency of utilized techniques, the variations of the proposed algorithm named EHNL(RSUP) and EHNL(TM) are introduced. The experimental results show that variants of the proposed algorithm mine the HUIs efficiently.
An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.
Mobile augmented reality learning objects in higher education

Teachers and learners in all sectors of education continue to have access to a growing number of mobile augmented reality (AR) applications for the creation and implementation of mobile AR experiences and learning objects (LOs). In this study, affordances of mobile AR and LOs for higher education are investigated through the mobile AR platform&nbsp;HP Reveal. Digital trace data from publicly shared and published AR users’ LOs were examined to investigate affordances of AR technology in educational organisations and institutions and their potential implications in areas of higher education. For this purpose, a quantitative comparative analysis of system data and content from 632 AR LOs was conducted at two instances over a 2-year interval period. Each LO was thematically coded to determine multimodal functionalities and characteristics. Further thematic coding and categorisation revealed four emergent categories for affordances in higher education:&nbsp;learner interaction, collaboration, cultural exploration&nbsp;and&nbsp;digital storytelling. Results also revealed increases over time in the use of recorded and online video content and the use of three-dimensional (3D) characters for educational purposes. An examination of the affordances offered by the AR platform revealed opportunities for educators to explore further interactive and collaborative uses of AR with their learners for pedagogical purposes in higher education. This paper is part of the special collection Mobile Mixed Reality Enhanced Learning, edited by ThomCochrane, Fiona Smart, Helen Farley and Vickel Narayan. More papers from this collection can befound here.
Image Splicing Localization via Semi-global Network and Fully Connected Conditional Random Fields

We address the problem of image splicing localization: given an input image, localizing the spliced region which is cut from another image. We formulate this as a classification task but, critically, instead of classifying the spliced region by local patch, we leverage the features from whole image and local patch together to classify patch. We call this structure Semi-Global Network. Our approach exploits the observation that the spliced region should not only highly relate to local features (spliced edges), but also global features (semantic information, illumination, etc.) from the whole image. Furthermore, we first integrate Fully Connected Conditional Random Fields as post-processing technique in image splicing to improve the consistency between the input image and the output of the network. We show that our method outperforms other state-of-the-art methods in three popular datasets.
Immersive Virtual Colonoscopy

Virtual colonoscopy (VC) is a non-invasive screening tool for colorectal polyps which employs volume visualization of a colon model reconstructed from a CT scan of the patient's abdomen. We present an immersive analytics system for VC which enhances and improves the traditional desktop VC through the use of VR technologies. Our system, using a head-mounted display (HMD), includes all of the standard VC features, such as the volume rendered endoluminal fly-through, measurement tool, bookmark modes, electronic biopsy, and slice views. The use of VR immersion, stereo, and wider field of view and field of regard has a positive effect on polyp search and analysis tasks in our immersive VC system, a volumetric-based immersive analytics application. Navigation includes enhanced automatic speed and direction controls, based on the user's head orientation, in conjunction with physical navigation for exploration of local proximity. In order to accommodate the resolution and frame rate requirements for HMDs, new rendering techniques have been developed, including mesh-assisted volume raycasting and a novel lighting paradigm. Feedback and further suggestions from expert radiologists show the promise of our system for immersive analysis for VC and encourage new avenues for exploring the use of VR in visualization systems for medical diagnosis.