Reviewerly/exHarmony huggingface dataset:
Shared digital experiences supporting collaborative meaning-making at heritage sites

A growing body of research testifies to the capacity for archaeological and other cultural heritage sites to generate wonder, attachment, personal transformation and restoration, family bonding and community building amongst their visitors. Using evaluation data from two related European Commission‐funded research projects, CHESS and EMOTIVE, we discuss here our work in developing mobile-based emotionally‐engaging digital stories for visitors to diverse cultural heritage sites. The sites range from world-renowned museums, such as the Acropolis Museum in Athens (Greece), to UNESCO World Heritage sites, such as the Catalhoyuk Neolithic archeological site (in Turkey). The evaluation studies feature detailed observations of visitors on‐site as well as post‐experience questionnaires and interviews, providing us with rich data on several axes; e.g. in relation to interactive story plot and narration, staging and wayfinding in the physical space, personalisation and social interaction. In this chapter, we specifically focus on shared experience and the impact that digital technology can have in promoting the cultural site as a social space. On the one hand, our findings testify that digital empathic stories can evoke narrative transportation, and even, in some cases, personal attachment and critical (self‐)reflection, which leads us to consider how their enchanting capacities might be pushed even further into the building of broader, collective social conscience. At the same time, the findings reveal the challenges, both conceptual and practical, of designing a shared digital experience in which visitors engage with the site and each other in meaningful ways.
Diagnosis of design and defects in radiography of ceramic antique objects using the wavelet-domain hidden Markov models

Radiography is often the method of choice for detection of hidden design details and defects in antique ceramic objects. To maximize information extraction from the images, as well as to optimize the operators’ image interpretation, the design and defect regions should be revealed. Development of effective image enhancement methods is an important research area in industrial radiographic testing. In this study, the wavelet-domain hidden Markov models were used to improve the hidden design detail and defect(s) visualization from the radiographs of the ceramic antique objects of Samiran region. The method relies on generating the frequency components of the image and removing noise from the low contrast component by means of the Markov method. The proposed algorithm was successfully applied to the radiographic images of the antique and cultural heritage objects. The design detail visualization and defect region detection were improved whilst preserving the object edge and fine detail imaging information. Experts’ reviews showed that the design details from the dual domain reconstructed images were better visualized than the original images. Also, different defects were better detected by the application of the method to the images.
Atlas: Application Confidentiality in Compromised Embedded Systems

Due to the requirements of the Internet-of-Things, modern embedded systems have become increasingly complex, running different applications. In order to protect their intellectual property as well as the confidentiality of sensitive data they process, these applications have to be isolated from each other. Traditional memory protection and memory management units provide such isolation, but rely on operating system support for their configuration. However, modern operating systems tend to be vulnerable and cannot guarantee confidentiality when compromised. We present Atlas, a hardware-based security architecture, complementary to traditional memory protection mechanisms, ensuring code and data confidentiality through transparent encryption, even when the system software has been exploited. Atlas relies on its zero-software trusted computing base to protect against system-level attackers and also supports secure shared memory. We implemented Atlas based on the LEON3 softcore processor, including toolchain extensions for developers. Our FPGA-based evaluation shows minimal cycle overhead at the cost of a reduced maximum frequency.
Confidence Test for Blind Analysis of BPSK Signals

A binary-hypothesis-test-based algorithm is proposed to assess the confidence of blind analysis results of binary phase-shift keying signals. The reference signal is first constructed based on the given analysis result. The spectrum of multiplication between observed signals and reference signals is used as a distinguishing statistic, and the decision based on the false alarm rate is accordingly taken to verify whether a specific analysis is confident or not. The theoretical analysis and simulation results validate the effectiveness of the proposed algorithm.
TraMNet - Transition Matrix Network for Efficient Action Tube Proposals

Current state-of-the-art methods solve spatio-temporal action localisation by extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate sets of temporally connected bounding boxes called action micro-tubes. However, they fail to consider that the underlying anchor proposal hypotheses should also move (transition) from frame to frame, as the actor or the camera do. Assuming we evaluate n 2D anchors in each frame, then the number of possible transitions from each 2D anchor to the next, for a sequence of f consecutive frames, is in the order of $$O(n^f)$$ , expensive even for small values of f. To avoid this problem we introduce a Transition-Matrix-based Network (TraMNet) which relies on computing transition probabilities between anchor proposals while maximising their overlap with ground truth bounding boxes across frames, and enforcing sparsity via a transition threshold. As the resulting transition matrix is sparse and stochastic, this reduces the proposal hypothesis search space from $$O(n^f)$$ to the cardinality of the thresholded matrix. At training time, transitions are specific to cell locations of the feature maps, so that a sparse (efficient) transition matrix is used to train the network. At test time, a denser transition matrix can be obtained either by decreasing the threshold or by adding to it all the relative transitions originating from any cell location, allowing the network to handle transitions in the test data that might not have been present in the training data, thus making detection translation-invariant. We show that our network is able to handle sparse annotations such as those available in the DALY dataset, while allowing for both dense (accurate) or sparse (efficient) evaluation within a single model. We report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24 datasets to support our claims.
Learning cross-modal correlations by exploring inter-word semantics and stacked co-attention

Cross-modal information retrieval aims to find heterogeneous data of various modalities from a given query of one modality. The main challenge is to learn the semantic correlations between different modalities and measure the distance across modalities. For text-image retrieval, existing work mostly uses off-the-shelf Convolutional Neural Network (CNN) for image feature extraction. For texts, word-level features such as bag-of-words or word2vec are employed to build deep learning models to represent texts. Besides word-level semantics, the semantic relations between words are also informative but less explored. In this paper, we explore the inter-word semantics by modelling texts by graphs using similarity measure based on word2vec. Besides feature presentations, we further study the problem of information imbalance between different modalities when describing the same semantics. For example textual descriptions often contain more background information that cannot be conveyed by images and vice versa. We propose a stacked co-attention network to progressively learn the mutually attended features of different modalities and enhance their fine-grained correlations. A dual-path neural network is proposed for cross-modal information retrieval. The model is trained by a pairwise similarity loss function to maximize the similarity of relevant text-image pairs and minimize the similarity of irrelevant pairs. Experimental results show that the proposed model outperforms the state-of-the-art methods significantly, with 19% improvement on accuracy for the best case.
Application of Virtual and Augmented Reality for Training and Mentoring of Higher Education Instructors

The purpose of this chapter is to specify various notions of virtual reality (VR) and augmented reality (AR) including the historical development and illustrate how they are applied to train and mentor instructors in higher education settings. This study also presents various industry-wide examples of utilizing VR and AR for training and education. Benefits and limitations of using VR and AR in academic settings are discussed as well. Additionally, this study provides up to date VR and AR applications that can be adopted in training and mentoring instructors of higher education. Based on a comparative analysis of available technologies, the authors suggest possible future research to improve existing practices in the use of AR and VR in training and mentoring instructors in higher education settings.
A novel recommendation approach based on chronological cohesive units in content consuming logs

We propose a novel recommendation approach based on chronological cohesive units (CCUs) of content consuming logs. Chronological cohesive units are defined as sub-sequences of logs in which items are highly related to each other. We first generate rules for splitting consuming logs into CCUs. We select features which are effective for splitting of consuming logs and combine them into a binary decision tree to generate splitting rules with genetic programming. With the rules, we split content consuming logs into CCUs, and identify strongly associated items in the CCUs. Next items are recommended with an association rule-based approach. The proposed method is evaluated using two-real datasets: web page navigation logs and movie consuming logs. The experiments confirm that the proposed approach is superior to the existing methods in various aspects such as hit ratio, click-soon ratio, sparsity, diversity and serendipity.
A simple and fast adaptive nonlocal multispectral filtering algorithm for efficient noise reduction in magnetic resonance imaging

We recently introduced a multispectral (MS) nonlocal (NL) filter based on maximum likelihood estimation (MLE) of voxel intensities, termed MS-NLML. While MS-NLML provides excellent noise reduction and improved image feature preservation as compared to other NL or MS filters, it requires considerable processing time, limiting its application in routine analyses. In this work, we introduced a fast, simple, and robust filter, termed nonlocal estimation of multispectral magnitudes (NESMA), for noise reduction in multispectral (MS) magnetic resonance imaging (MRI).Through extensive simulation and in-vivo analyses, we compared the performance of NESMA and MS-NLML in terms of noise reduction and processing efficiency. Further, we introduce two simple adaptive methods that permit spatial variation of similar voxels, R, used in the filtering. The first method is semi-adaptive and permits variation of R across the image by using a relative Euclidean distance (RED) similarity threshold. The second method is fully adaptive and filters the raw data with several RED similarity thresholds to spatially determine the optimal threshold value using an unbiased criterion.NESMA shows very similar filtering performance as compared to MS-NLML, however, with much simple implementation and very fast processing time. Further, for both filters, the adaptive methods were shown to further reduce noise in comparison with the conventional non-adaptive method in which R is set to a constant value throughout the image.NESMA is fast, robust, and straightforward to implement filter. These features render it suitable for routine clinical use and analysis of large MRI datasets.
A novel cloud model based data placement strategy for data-intensive application in clouds

Today, more and more data-intensive applications are deployed in cloud environments among geo-distributed data centers. It is a fundamental challenge to solve the data placement problem for data-intensive applications. The key problem is to present the uncertain and random process of data placement appropriately, while significantly reducing the transmission time across data centers and decreasing the frequency of data movement among data centers. In this paper, we introduce a new type of entity called a Virtual Data Agent (VDA) and convert the data placement problem into two mapping processes, namely, mapping from the data set to the virtual data agent and mapping from the virtual data agent to the data center. We propose Cloud model based Data Placement Algorithm with Virtual Data Agent (CDPVDA). Through simulation using real workflow applications, we compare CDPVDA with two typical data placement strategies. The results indicate that CDPVDA could reduce unavoidable overhead in data transmission between the data centers by 5% to 20%.
Visual Analysis of Spatia-temporal Relations of Pairwise Attributes in Unsteady Flow

Despite significant advances in the analysis and visualization of unsteady flow, the interpretation of it's behavior still remains a challenge. In this work, we focus on the linear correlation and non-linear dependency of different physical attributes of unsteady flows to aid their study from a new perspective. Specifically, we extend the existing spatial correlation quantification, i.e. the Local Correlation Coefficient (LCC), to the spatio-temporal domain to study the correlation of attribute-pairs from both the Eulerian and Lagrangian views. To study the dependency among attributes, which need not be linear, we extend and compute the mutual information (MI) among attributes over time. To help visualize and interpret the derived correlation and dependency among attributes associated with a particle, we encode the correlation and dependency values on individual pathlines. Finally, to utilize the correlation and MI computation results to identify regions with interesting flow behavior, we propose a segmentation strategy of the flow domain based on the ranking of the strength of the attributes relations. We have applied our correlation and dependency metrics to a number of 2D and 3D unsteady flows with varying spatio-temporal kernel sizes to demonstrate and assess their effectiveness.
From Lexical Functional Grammar to enhanced Universal Dependencies

The paper describes the conversion of an LFG treebank of Polish into enhanced Universal Dependencies, and—more generally—identifies the kinds of information lost in translation from LFG to UD. The paper also presents the resulting UD treebank of Polish and compares it to the previous UD treebank of Polish.
Photobomb Defusal Expert: Automatically Remove Distracting People From Photos

Cropping is one of the main operations for removing unwanted or distracting elements in an image. It can portray the main subject in a better layout and enhance the image aesthetic for a better visual experience. However, manually cropping multiple images are tedious and time consuming. It also requires some amount of artistic skill to determine a good way to crop the image. In this paper, we propose an automatic photo cropping system that determines the optimal bounding box for cropping to produce aesthetically pleasing images. Our system also finds and removes distracting people to place the focus on the main subject. We combined both learned internal image representations using a convolutional autoencoder as well as manually extracted features to train our model. Experimental results of our system achieved significantly better performance compared to other existing automatic cropping methods.
Relative Vehicle Displacement Approach for Path Tracking Adaptive Controller With Multisampling Data Transmission

This paper proposes a cyber-physical framework for vision-based automated vehicle path tracking. The framework has been developed in two parts: first, an intelligent relative vehicle displacement approach with an adaptive neural network controller; and second, multisampling data transmission architecture for any sensor-controller-actuator network. Uncertainties due to illumination effects, occlusion, and obscure images affect system performance drastically. The proposed relative vehicle displacement approach takes care of these uncertainties. The adaptive neural network controller generates precise control actions for stabilizing the system in minimum time. A reliable and robust data transmission architecture is of utmost importance for any Internet of Things application. Successful data transmission depends on several parameters, such as delay, communication channel behavior, and packet loss. A novel multisampling data transmission architecture addressing these issues has been proposed in this paper. Various cases of data transmission have been demonstrated on the time critical application of vision-based tracking by an automated guided vehicle. The results of real-time path tracking operation have been duly compared with other control techniques and data transmission architecture. The experimental results show the efficiency of the proposed system.
Cognitive complexity as a quantifier of version to version Java-based source code change: An empirical probe

It has been often argued that it is challenging to modify code fragments from existing software that contains files that are difficult to comprehend. Since systematic software maintenance includes an extensive human activity, cognitive complexity is one of the intrinsic factors that could potentially contribute to or impede an efficient software maintenance practice, the empirical validation of which remains vastly unaddressed. This study conducts an experimental analysis in which the software developer's level of difficulty in comprehending the software: the cognitive complexity, is theoretically computed and empirically evaluated for estimating its relevance to actual software change. For multiple successive releases of two Java-based software projects, where the source code of a previous release has been substantively used in a novel release, we calculate the change results and the values of the cognitive complexity for each of the version's source code Java files. We construct eight datasets and build predictive models using statistical analysis and machine learning techniques. The pragmatic comparative examination of the estimated cognitive complexity against prevailing metrics of software change and software complexity clearly validates the cognitive complexity metric as a noteworthy measure of version to version source code change.
Evaluation model for business sites planning based on online and offline datasets

In recent years, along with the growth of the mobile internet and the internet of things (IOT), Big Data of city has drawn the attention of many researchers. Combining heterogeneous datasets to evaluate the planning of a new business site is critical for the success of an investment. The traditional approaches primarily solved the problem of ranking offline geographic placements. However, with the prosperity of a new business model, Online to Offline (O2O), individuals’ behavior is affected by the mobile internet, which is gradually changing traditional business. As a large quantity of information is accessible on the mobile internet, individuals have an enormous number of choices never seen before. Hence, planning a new business site should also consider the online business strategy. Recently, new datasets from O2O platforms provided relevant insights. In this paper, the hotel business is chosen for analysis. By exploring new datasets, numerous features are extracted for a detailed visualization and analysis. The extracted features can be divided into three categories: geographic, business strategic and mobility. Subsequently, through the fusion of different features, models are devised to evaluate the planning of a new hotel. According to the experimental results, the valid features and models proposed can not only better solve the traditional problem of geographic placement ranking but also provide a simple prediction of hotels’ trading volumes according to their business strategies. It provides a general and overall evaluation for planning a new business site in the era of the mobile internet.
X-ray fluorescence scanning of discrete samples – An economical perspective

We measured discrete, powdered, homogenized samples from geochemical reference standards (GRS) and from a loess-paleosol sequence (LPS) with an ITRAX XRF core-scanner. The objective is to highlight cost- and time-efficiency of X-ray fluorescence (XRF) scanning of discrete samples in comparison to conventional XRF. Based on data precision (mainly < 4% mean relative standard deviation) and data reliability (often R2 ≥ 0.9) tests with six different measurement times (1, 5, 10, 30, 60 and 100 s) and two different X-ray tubes (Mo, Cr), reasonable minimal measurement times for thirteen considered elements were assessed. Accordingly, we designed test case scenarios for 10, 30 and 100 s measurement time in comparison to conventional XRF analysis with regard to cost- and time-efficiency. As long as qualitative geochemical data are sufficient for scientific research questions at hand, XRF scanning of discrete samples is proven to be more efficient by factors of 15–23 as to time and by factors of 4.5–51 as to cost when compared with conventional wavelength-dispersive (WD)-XRF analysis commonly applied to discrete samples. This underlines XRF scanning of discrete samples as a versatile, non-destructive qualitative geochemical screening tool with unprecedented time- and cost-efficiency. Such efficiency will foster application of this technique in a broad scientific field including e.g. terrestrial paleoenvironmental reconstructions and/or environmental pollution studies.
The effect of answer validation on the performance of Question-Answering systems

Question Answering (QA) systems help users to find relevant information. However, current QA systems still return several incorrect answers, which decreases the confidence of users in such systems. Some researchers have suggested the inclusion of validation in QA with the purpose of reducing wrong responses and, therefore, improving results. Nevertheless, there has not been a precise study about how validation contributes to QA results. In this paper, we include a new analysis of the impact of validation on QA results. We reuse data from real QA systems and show that validation enables QA systems to reduce incorrect answers and obtain better results. We think this study will allow researchers to develop better QA systems.
Tutorial on the generation of ergodic trajectories with projection‐based gradient descent

A vehicle trajectory is ergodic with respect to some spatial distribution if the time spent in a region is proportional to the region's integrated density. One method of generating ergodic trajectories is projection-based trajectory optimisation, a gradient descent method that supports non-linear dynamics and balances trajectory ergodicity with control effort. In this study, the authors survey the existing literature on projection-based trajectory generation, focusing on implementation. They introduce an easy-to-use, open-source software package that generates ergodic trajectories orders of magnitude faster than previously reported. The authors’ goal is to simplify the implementation of projection-based ergodic control so it can be applied widely across disciplines.