Reviewerly/exHarmony huggingface dataset:
Symbolic Disintegration with a Variety of Base Measures

Disintegration is a relation on measures and a transformation on probabilistic programs that generalizes density calculation and conditioning, two operations widely used for exact and approximate inference. Existing program transformations that find a disintegration or density automatically are limited to a fixed base measure that is an independent product of Lebesgue and counting measures, so they are of no help in practical cases that require tricky reasoning about other base measures. We present the first disintegrator that handles variable base measures, including discrete-continuous mixtures , dependent products , and disjoint sums . By analogy with type inference, our disintegrator can check a given base measure as well as infer an unknown one that is principal. We derive the disintegrator and prove it sound by equational reasoning from semantic specifications. It succeeds in a variety of applications where disintegration and density calculation had not been previously mechanized.
Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

At present, the existing abnormal event detection models based on deep learning mainly focus on data represented by a vectorial form, which pay little attention to the impact of the internal structure characteristics of feature vector. In addition, a single classifier is difficult to ensure the accuracy of classification. In order to address the above issues, we propose an abnormal event detection hybrid modulation method via feature expectation subgraph calibrating classification in video surveillance scenes in this paper. Our main contribution is to calibrate the classification of a single classifier by constructing feature expectation subgraphs. First, we employ convolutional neural network and long short-term memory models to extract the spatiotemporal features of video frame, and then construct the feature expectation subgraph for each key frame of every video, which could be used to capture the internal sequential and topological relational characteristics of structured feature vector. Second, we project expectation subgraphs on the sparse vector to combine with a support vector classifier to calibrate the results of a linear support vector classifier. Finally, the experiments on a common dataset named UCSDped1 and a coal mining video dataset in comparison with some existing works demonstrate that the performance of the proposed method is better than several the state-of-the-art approaches.
Joint Model of Entity Recognition and Relation Extraction with Self-attention Mechanism

In recent years, the joint model of entity recognition (ER) and relation extraction (RE) has attracted more and more attention in the healthcare and medical domains. However, there are some problems with the prior work. The joint model cannot extract all the relations for a specific entity, and the majority of joint models heavily rely on complex artificial features or professional natural language processing (NLP) tools. In this article, we construct a novel joint model that can simultaneously extract all medical entities and relations from medicine Chinese instructions. Moreover, the self-attention mechanism is introduced to the joint model to learn word intra-sentence dependencies. The proposed model is evaluated using a medicine Chinese instruction dataset that we collect and an open dataset provided in CoNLL-2004. Experimental results show that the model with self-attention achieves the state-of-the-art performance.
Fast Query-by-example Speech Search using Attention-based Deep Binary Embeddings

State-of-the-art query-by-example (QbE) speech search approaches usually use recurrent neural network (RNN) based acoustic word embeddings (AWEs) to represent variable-length speech segments with fixed-dimensional vectors, and thus simple cosine distances can be measured over the embedded vectors of both the spoken query and the search content. In this paper, we aim to improve search accuracy and speed for the AWE-based QbE approach in low-resource scenario. First, multi-head self-attentive mechanism is introduced for learning a sequence of attention weights for all time steps of RNN outputs while attending to different positions of a speech segment. Second, as the real-valued AWEs suffer from substantial computation in similarity measure, a hashing layer is adopted for learning deep binary embeddings, and thus binary pattern matching can be directly used for fast QbE speech search. The proposed approach of self-attentive deep hashing network is effectively trained with three specifically-designed objectives: a penalization term, a triplet loss, and a quantization loss. Experiments show that our approach improves the relative search speed by 8 times and mean average precision (MAP) by 18.9%, as compared with the previous best real-valued embedding approach.
New ways of seeing, feeling, being: intimate encounters in virtual reality performance

Robin Nelson has argued that intermedial performance has ‘elicited a new cultural way of seeing, feeling and being in the contemporary world’ (Nelson in Bay-Cheng et al. 2010 Bay-Cheng, Sarah, Chiel Kattenbelt, Andy Lavender and Robin Nelson. 2010. Mapping Intermediality in Performance. Amsterdam: Amsterdam University Press.[Crossref] , [Google Scholar]. Mapping Intermediality in Performance. Amsterdam: Amsterdam University Press, 18). This extension of John Berger’s ‘ways of seeing’ to consider feeling and being in relation to visual art and performance becomes increasingly important in works that immerse the spectator’s body in physical and/or virtual environments. This article considers ways of seeing, feeling and being in three virtual reality performances staged in 2019: Jordan Tannahill’s Draw Me Close (Young Vic, Feb 2019); Marshmallow Laser Feast’s We Live in an Ocean of Air (Saatchi Gallery, 2018–2019); and my own practice-as-research explorations of VR and intimate performance, and our faces, my heart, brief as photos, that took place during a residency at the National Theatre of Scotland in August and September 2019. In this article, I argue that the specific modes of engagement produced in these works are the unique result of the meeting point between virtual reality technologies and live performance practices and that the forms of embodied spectatorship afforded can facilitate the movement between the actual and the virtual, intimacy and distance, immersion and making strange: producing very real and potent effects.
ShArc: A Geometric Technique for Multi-Bend/Shape Sensing

We present ShArc, a precision, geometric measurement technique for building multi-bend/shape sensors. ShArc sensors are made from flexible strips that can be dynamically formed into complex curves in a plane. They measure local curvature by noting the relative shift between the inner and outer layers of the sensor at many points and model shape as a series of connected arcs. Unlike jointed systems where angular errors sum with each joint measured, ShArc sensors do not accumulate angular error as more measurement points are added. This allows for inexpensive, robust sensors that can accurately model curves with multiple bends. To demonstrate the efficacy of this technique, we developed a capacitive ShArc sensor and evaluated its performance. We conclude with examples of how ShArc sensors can be employed in applications like gesture input devices, user interface controllers, human motion tracking and angular measurement of free-form objects.
A quantum key distribution protocol for rapid denial of service detection

Abstract We introduce a quantum key distribution protocol designed to expose fake users that connect to Alice or Bob for the purpose of monopolising the link and denying service. It inherently resists attempts to exhaust Alice and Bob’s initial shared secret and is 100% efficient, regardless of the number of qubits exchanged above the finite key limit. Additionally, secure key can be generated from two-photon pulses without having to make any extra modifications. This is made possible by relaxing the security of BB84 to that of the quantum-safe block cipher used for day-to-day encryption, meaning the overall security remains unaffected for useful real-world cryptosystems such as AES-GCM being keyed with quantum devices.
Inarticulate Devices

Research through design (RTD) is commonly conceived as a material and discursive practice of articulating knowledge. This paper contributes to the understanding of RTD as a form of critical inquiry by considering how inarticulacy can also be a productive element of this process. We present two reflective accounts of critically-engaged RTD practices in which our attempts to articulate concerns or questions were met with resistance from technology that was both the subject and medium of our investigation. We argue that encountering inarticulacy is not a failure of RTD but instead points to how material exploration can sensitise us to how network technology resists articulating certain values or concerns. Encountering inarticulacy led us to formulate new problems and new lines of inquiry. We conclude by suggesting that the central role given to ambiguity in RTD prepares us to witness and respond to inarticulacy in our practices, design outcomes and critical understandings.
Sentiment key frame extraction in user-generated micro-videos via low-rank and sparse representation

It is a prevailing trend that the user-generated content (UGC) on social media is shifting toward mobile video and micro content. Sentiment analysis and emotion recognition extract the opinions expressed in UGC and are important to understand the fast-growing mobile micro-videos. Although extensive research efforts have been devoted to it, most of the existing studies pre-processed the video before extracting sentiment features, pre-sampling as an example. Due to the rather unstructured nature of user-generated videos and the sparsely expressed emotions, sampling before sentiment analysis can remove visual contents important to understand the video. In this paper, a novel unsupervised sentiment key frame extraction (SKFE) model based on low-rank and sparse representation is proposed. Sparsity is the important characteristic distinct to a video frame. The low-rank constraint is helpful to improve the robustness to noise and outliers. The l2,1-norm sparsity is adopted to improve the robustness via different indexes running through the feature dimensions and among the video frames. To better characterize the relationship between the local features and alleviate the sensitiveness of sparse coding, a regularization term based on the Laplacian matrix is introduced to preserve the consistency of sparse codes for similar local features. The experimental results on publicly available datasets demonstrate the effectiveness of the proposed SKFE model.
Towards Factorized SVM with Gaussian Kernels over Normalized Data

There is an emerging trend of integrating machine learning (ML) techniques into database systems (DB). Considering that almost all the ML toolkits assume that the input of ML algorithms is a single table even though many real-world datasets are stored as multiple tables due to normalization in DB. Thus, data scientists have to perform joins before learning a ML model. This strategy is called learning after joins, which incurs redundancy avoided by normalization. In the area of ML, the Support Vector Machine (SVM) is one of the most standard classification tools. In this paper, we focus on the factorized SVM with gaussian kernels over normalized data. We present factorized learning approaches for two main SVM optimization methods, i.e., Gradient Descent (GD) and Sequential Minimal Optimization (SMO), by factorizing gaussian kernel function computation. Furthermore, we transform the normalized data into matrices, and boost the efficiency of SVM learning via linear algebra operations. Extensive experiments with nine real normalized data sets demonstrate the efficiency and scalability of our proposed approaches.
An Augmented Reality Tool for Teaching Application in the Agronomy Domain

Nowadays, the combination of new technologies and the use of mobile devices opens up a new range of teaching–learning strategies in different agricultural engineering degrees. This article presents an augmented reality tool that allows for improved spatial viewing for students who have certain difficulties with viewing graphic representations of agronomic systems and devices. This tool is known as ARTID (Augmented Reality for Teaching, Innovation and Design) and consists in a free-access mobile application for devices using the Android operating system. The proposed method provides each exploded drawing or overall drawing with a QR code that can be used by students to view their 3D models by augmented reality in their own mobile devices. An evaluation experience was carried out to assess the validity of the tool on different devices and the acceptance and satisfaction level of this kind of resources in subjects of graphic expression in engineering. Finally, an example of application in the agronomic domain is provided by the 3D virtual model of portable ferticontrol equipment that comprises the different structures and tanks, which, if viewed by conventional graphical representations, may entail a certain level of difficulty. Thanks to this tool, reality can be merged with the virtual world to help favour the understanding of certain concepts and to increase student motivation in agronomy studies.
Too Hot to Handle: An Evaluation of the Effect of Thermal Visual Representation on User Grasping Interaction in Virtual Reality

Influence of interaction fidelity and rendering quality on perceived user experience have been largely explored in Virtual Reality (VR). However, differences in interaction choices triggered by these rendering cues have not yet been explored. We present a study analysing the effect of thermal visual cues and contextual information on 50 participants' approach to grasp and move a virtual mug. This study comprises 3 different temperature cues (baseline empty, hot and cold) and 4 contextual representations; all embedded in a VR scenario. We evaluate 2 different hand representations (abstract and human) to assess grasp metrics. Results show temperature cues influenced grasp location, with the mug handle being predominantly grasped with a smaller grasp aperture for the hot condition, while the body and top were preferred for baseline and cold conditions.
Understanding Parenting Stress through Co-designed Self-Trackers

New parents often experience significant stress as they take on new roles and responsibilities. Stress management and mental wellbeing are two areas in which personal informatics (PI) research has gained attention, and there is an opportunity to investigate how parenting stress can be mitigated through PI practices. In this paper, we present the results of a co-designed technology probe study through which we deployed individualized self-trackers with new parents. We investigate the stress management topics new parents are interested in tracking and how — and with what goals---they engage in self-directed PI practices. Our findings indicate that PI practices can potentially enable parents to: re-discover positive aspects of their everyday lives; identify better-suited stress management strategies; and facilitate spousal communication about shared responsibilities. We discuss how self-tracking experiences for the mental wellness of parents can be better designed.
Call to interact

Interactive public displays are becoming increasingly commonplace. Two important challenges for these displays exist: 1) how interactivity is communicated to the passer-by and 2) once the audience have engaged, how the interactive affordances are communicated. We explore these challenges through the lens of a new emerging interaction modality - contactless mid-air gesture controlled public displays. This paper presents several solutions to the problems above, inspired by the findings existing within the related research on public displays. These solutions are deployed in an in-the-wild setting - a cinema for an 8 week duration - and evaluated through observational and quantitative research. The data forms a set of design guidelines for successfully attracting the passer-by and conveying the affordances of the contactless gestural public displays.
Mixed Reality Books: Applying Augmented and Virtual Reality in Mining Engineering Education

This chapter deals with the integration of Augmented and Virtual Reality (AR/VR) elements into academic mining education. The focus lies on the didactical approach within the EU-funded MiReBooks (Mixed Reality Books) project. The project aims to develop a series of AR- and VR-based interactive mining manuals as a new digital standard for higher education across European engineering education. By combining AR and VR technologies, it is possible to address current challenges in mining education in an innovative way. These virtual applications should make otherwise impossible and dangerous situations accessible to students. Classical paper-based teaching materials are enriched with AR content and translated into pedagogically and didactically coherent manuals for integrative use in the classroom. The authors explore how AR and VR instruments can be effectively integrated into teaching. The results of a broad evaluation of AR/VR-based lectures are presented and discussed in this chapter. The experiences and findings are summarized in a decision matrix for the use of AR/VR-based technologies in teaching.
Usability evaluation of university website: a case study

Abstract Web-based public services become an important part in supporting the success of a university, but there are still many difficulties to use it. This study was conducted to evaluate whether Muhammadiyah Magelang University website has had the acceptability criteria of usability testing. The study was conducted using a questionnaire as a research instrument that consisted of 17 questions and filled by 95 respondents. Those questions grouped into five variables usability, there are learnability, efficiency, memorability, error, and satisfaction. Based on analysis, the result said that the mean score of overall usability testing to measure website usage Muhammadiyah Magelang University website was 2.77, for each variable the Learnability aspects have the overall score of 2.83, the efficiency was 2.73, memorability was 2.82, an error was 2.65 and 2.79 for satisfaction by respondents. From these results indicate that the website of Muhammadiyah Magelang University already Quite Easy to use. However, there are still many things that need to be fixed, especially in case of errors, focused on the availability of the feature and efficiency aspect in the speed of accessing the feature for getting information.
Transition Activity Recognition System Based on Standard Deviation Trend Analysis

With the development and popularity of micro-electromechanical systems (MEMS) and smartphones, sensor-based human activity recognition (HAR) has been widely applied. Although various kinds of HAR systems have achieved outstanding results, there are still issues to be solved in this field, such as transition activities, which means the transitional process between two different basic activities, discussed in this paper. In this paper, we design an algorithm based on standard deviation trend analysis (STD-TA) for recognizing transition activity. Compared with other methods, which directly take them as basic activities, our method achieves a better overall performance: the accuracy is over 80% on real data.
Conversation-Based School Building Inspection Support System

A conversation-based system is proposed for supporting assessors in performing existing school building inspections. School building safety is a pressing issue; however, some difficulties in the overall process require solutions or improvements, including the complexity of building inspection tasks, the restrictions posed by the paperwork process, and the ineffectiveness of the management of existing school building inspections. In this study, we developed a conversation-based building inspection support system to reduce such problems, with the proposed system notifying and guiding assessors to complete building inspections, combined with a dashboard for managers to consume reports to determine whether further assessments or retrofits are required. The process of school building inspections was digitalized, with a chatbot implemented that features notifications either according to a routine inspection schedule or postseismic events, a conversation-based interface for guiding nonprofessional assessors, the integration of intuitive activation of inspections after receiving notifications, the use of multimedia to show damage directly without the possibility of mistakes, and data visualization for supporting managerial decision-making to enhance the quality and accuracy of budget allocation.
SCHAIN-IRAM: An Efficient and Effective Semi-Supervised Clustering Algorithm for Attributed Heterogeneous Information Networks

A heterogeneous information network (HIN) is one whose nodes model objects of different types and whose links model objects’ relationships. To enrich its information, objects in an HIN are typically associated with additional attributes. We call such an HIN an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Attributed HIN</i> or AHIN. We study the problem of clustering objects in an AHIN, taking into account objects’ similarities with respect to both object attribute values and their structural connectedness in the network. We show how supervision signal, expressed in the form of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">must-link set</i> and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cannot-link set</i> , can be leveraged to improve clustering results. We put forward the SCHAIN algorithm to solve the clustering problem, and two highly efficient variants, SCHAIN-PI and SCHAIN-IRAM, which employ the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">power iteration based method</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implicitly restarted Arnoldi method</i> respectively to compute eigenvectors of a matrix. We conduct extensive experiments comparing SCHAIN-based algorithms with other state-of-the-art clustering algorithms. Our results show that SCHAIN-IRAM outperforms other competitors in terms of clustering effectiveness and is highly efficient.
AURORA: Data-driven Construction of Visual Graph Query Interfaces for Graph Databases

Several commercial and academic frameworks for querying a large collection of small- or medium-sized data graphs (eg. chemical compounds) provide visual graph query interfaces (a.k.a GUI) to facilitate non-programmers to query these sources. However, construction of these visual interfaces is not data-driven. That is, it does not exploit the underlying data graphs to automatically generate the contents of various panels in a GUI. Such data-driven construction has several benefits such as facilitating efficient subgraph query formulation and portability of the interface across different application domains and sources. In this demonstration, we present a novel data-driven visual subgraph query interface construction engine called AURORA. Specifically, given a graph repository D containing a collection of small- or medium-sized data graphs, it automatically generates the GUI for D by populating various components of the interface. We demonstrate various innovative features of AURORA.