Reviewerly/exHarmony huggingface dataset:
Swarm Path Planning for the Deployment of Drones in Emergency Response Missions

The rapid development of Unmanned Aerial Vehicles (UAV) technologies over the recent years has been decisive for their integration in emergency response missions. While initial use by first responders focused on manual operations, the need to improve utilization necessitates higher levels of automation.Contributing towards that end-goal, this work derives swarm path planning algorithms that can effectively and efficiently be employed to search and monitor the operating field. A swarm is composed by two or more units, that coordinate to achieve the mission objectives including minimizing search time while ensuring coverage of the field. A graph theoretic approach is followed to model the underlying swarm path planning problem and mathematical programming is employed to describe a number of important variants of the cooperative strategies that arise. Thereafter, 4 algorithms are derived to solve the swarm path planning problem that are computationally efficient to implement and use in practice. A thorough performance evaluation is conducted to understand the advantages and disadvantages of each heuristic using a number of key performance metrics.
Tree-Encoded Bitmaps

We propose a novel method to represent compressed bitmaps. Similarly to existing bitmap compression schemes, we exploit the compression potential of bitmaps populated with consecutive identical bits, i.e., 0-runs and 1-runs. But in contrast to prior work, our approach employs a binary tree structure to represent runs of various lengths. Leaf nodes in the upper tree levels thereby represent longer runs, and vice versa. The tree-based representation results in high compression ratios and enables efficient random access, which in turn allows for the fast intersection of bitmaps. Our experimental analysis with randomly generated bitmaps shows that our approach significantly improves over state-of-the-art compression techniques when bitmaps are dense and/or only barely clustered. Further, we evaluate our approach with real-world data sets, showing that our tree-encoded bitmaps can save up to one third of the space over existing techniques.
What's Race Got To Do With It? Engaging in Race in HCI

There is an urgent and ongoing need to engage critically with race in human-computer interaction. In this workshop, we consider two intertwining aspects: first, how HCI research and practice should engage with race; second, how the HCI community itself can become more racially inclusive and equitable. The workshop offers a safe space for HCI scholars and practitioners to discuss how their experiences with race and racism impact their research and work life. Insights from critical race theory will inform the discussion. Workshop participants will draft a set of guidelines for research and a set of recommendations for SIGCHI leadership and the CHI community.
Using Cultural Probes in HCI4D/ICTD: A Design Case Study from Bungoma, Kenya

Cultural probes have long been used in HCI to provide designers with glimpses into the local cultures for which they are designing, and thereby inspire novel design proposals. HCI4D/ICTD researchers are increasingly interested in more deeply understanding local cultures in the developing regions where they work, in designing technologies that are not strictly related to socioeconomic development, and in considering new design approaches. However, few use this subjective, design-led method in their research. In this paper, I present a case study detailing my experience designing and deploying cultural probes in Bungoma, Kenya. Returns from my comment cards and digital camera activities draw attention to probe recipients' unique experiences and to Bungoma's distinctive characteristics; they also inspired a series of speculative design proposals. My experience motivates a discussion that elaborates on how a cultural probes approach can benefit HCI4D/ICTD research by raising questions about generalizability, objectivity, and the pursuit of a single solution in design. More broadly, I offer a case study demonstrating an alternative way to approach design in HCI4D/ICTD.
Autonomous Under Water Vehicle Based on Extreme Learning Machine for Sensor Fault Diagnosistics

The autonomous under water vehicle (AUV) system uses sensors which have a very important role in this field, even work in very complex environment. In order to improve the reliability of the AUV systems it important to know about the sensor failure diagnostic technology. In this paper two methods has been proposed they are combining phase space reconstruction and extreme learning machine(ELM).They are mostly being used to predict the sensor output in order to achieve the sensor fault diagnosis for AUVS. This stimulation experiments based on sea trial data shows the proposed method can be diagnosed by the sensor faults and recover the signals after the faults occur in a period of time
Divining Insights: Visual Analytics Through Cartomancy

Our interactions with data, visual analytics included, are increasingly shaped by automated or algorithmic systems. An open question is how to give analysts the tools to interpret these "automatic insights" while also inculcating critical engagement with algorithmic analysis. We present a system, Sortilège, that uses the metaphor of a Tarot card reading to provide an overview of automatically detected patterns in data in a way that is meant to encourage critique, reflection, and healthy skepticism.
Computational Modeling of Prefrontal Cortex for Meta-Cognition of a Humanoid Robot

For robot intelligence and human-robot interaction (HRI), complex decision-making, interpretation, and adaptive planning processes are great challenges.These require recursive task processing and meta-cognitive reasoning mechanism.Naturally, the human brain realizes these cognitive skills by prefrontal cortex which is a part of the neocortex.Previous studies about neurocognitive robotics would not meet these requirements.Thus, it is aimed at developing a brain-inspired robot control architecture that performs spatialtemporal and emotional reasoning.In this study, we present a novel solution that covers a computational model of the prefrontal cortex for humanoid robots.Computational mechanisms are mainly placed on the bio-physical plausible neural structures embodied in different dynamics.The main components of the system are composed of several computational modules including dorsolateral, ventrolateral, anterior, and medial prefrontal regions.Also, it is responsible for organizing the working memory.A reinforcement meta-learning based explainable artificial intelligence (xAI) procedure is applied to the working memory regions of the computational prefrontal cortex model.Experimental evaluation and verification tests are processed by the developed software framework embodied in the humanoid robot platform.The humanoid robots' perceptual states and cognitive processes including emotion, attention, and intention-based reasoning skills can be observed and controlled via the developed software.Several interaction scenarios are implemented to monitor and evaluate the model's performance.
An Empirical Comparison of Technologically Mediated Advertising in Under-connected Populations

Information and Communication Technology interventions have the potential to improve outcomes in health and other development sectors in low-income settings. Large-scale impact, however, remains the central challenge for the HCI4D community as significant and diverse resources are typically required to scale such interventions beyond the pilot stage. In contrast, voice-based entertainment services accessible over simple phones, designed for similarly low-income, low-literate populations manage to scale 'virally' to tens of thousands of users with little to no advertising cost. Our study compares the outcomes of using voice-based entertainment to spread a maternal-health hotline against conventional advertisement channels including paper flyers, posters, radio, TV, social media and robocalls. Through an 11-week deployment in Pakistan where the hotline reached 21,770 users over 32,625 calls, we find that the entertainment service outperformed other channels on all popular user acquisition metrics, with the exception of robocalls, which lead in terms of spread.
Modal Intuitionistic Logics as Dialgebraic Logics

Duality is one of the key techniques in the categorical treatment of modal logics. From the duality between (modal) algebras and (descriptive) frames one derives e.g. completeness (via a syntactic characterisation of algebras) or definability (using a suitable version of the Goldblatt-Thomason theorem). This is by now well understood for classical modal logics and modal logics based on distributive lattices, via extensions of Stone and Priestley duality, respectively. What is conspicuously absent is a comprehensive treatment of modal intuitionistic logic. This is the gap we are closing in this paper. Our main conceptual insight is that modal intuitionistic logics do not appear as algebra/coalgebra dualities, but instead arise naturally as dialgebras. Our technical contribution is the development of dualities for dialgebras, together with their logics, that instantiate to large class of modal intuitionistic logics and their frames as special cases. We derive completeness and expressiveness results in this general case. For modal intuitionistic logic, this systematises the existing treatment in the literature.
User-item content awareness in matrix factorization based collaborative recommender systems

Recommender systems promote sales of products and services by helping users alleviate the information overload problem. Collaborative filtering is most extensively used approach to design recommender system. The main idea of collaborative filtering i
Characterising the Quality of Behaviour Driven Development Specifications

Behaviour Driven Development (BDD) is an agile testing technique that enables software requirements to be specified as example interactions with the system, using structured natural language. While (in theory) being readable by non-technical stakeholders, the examples can also be executed against the code base to identify behaviours that are not yet correctly implemented. Writing good BDD suites, however, is challenging. A typical suite can contain hundreds of individual scenarios, that must correctly specify the system as a whole as well as individually. Despite much discussion amongst practitioners and in the blogosphere, as yet no formal definition of what makes for a high quality BDD suite has been given. To shed light on this, we surveyed BDD practitioners, asking for their opinions on the quality criteria that are important for BDD suites. We proposed, and asked for opinions on, four quality principles, and gave practitioners the option to add more principles of their own. This paper reports on the results of the survey, and presents an approach to defining BDD suite quality.
The Implementation of Deep Learning Using Convolutional Neural Network to Classify Based on Stomata Microscopic Image of Curcuma Herbal Plants

Abstract There are so many types of herbal plants that come from the same genus. The similarity of features in herbal plants makes it difficult to distinguish. Especially in the pharmaceutical field, which is very risky for making mistakes. The part of plants that is often used as ingredients for herbal medicines is the leaves, so research on leaves and their constituent organelles is very important for the pharmaceutical world. Therefore, an approach is needed to identify the types of organelles present in the leaves, where the organelles most frequently studied are the stomata. So, a neural network approach is needed to distinguish plant characteristics in the same genus. In this research there are 2 species of plants in the Curcuma genus, namely turmeric and ginger. This research was conducted by implementing Deep Learning using Convolutional Neural Network (CNN) with the help of the Gabor filter process and feature extraction using the Gray Level Co-occurrence Matrix (GLCM). The study was conducted using 160 microscopic images of Curcuma herbal plants as training data with training accuracy of 93.1% and test data of 40 images with an accuracy of 92.5%.
Crowdsourcing-based Data Extraction from Visualization Charts

Visualization charts are widely utilized for presenting structured data. Under many circumstances, people want to explore the data in the charts collected from various sources, such as papers and websites, so as to further analyzing the data or creating new charts. However, the existing automatic and semi-automatic approaches are not always effective due to the variety of charts. In this paper, we introduce a crowdsourcing approach that leverages human ability to extract data from visualization charts. There are several challenges. The first one is how to avoid tedious human interaction with charts and design simple crowdsourcing tasks. Second, it is challenging to evaluate worker's quality for truth inference, because workers may not only provide inaccurate values but also misalign values to wrong data series. To address the challenges, we design an effective crowdsourcing task scheme that splits a chart into simple micro-tasks. We introduce a novel worker quality model by considering worker's accuracy and task difficulty. We also devise an effective early-stopping mechanisms to save the cost. We have conducted experiments on a real crowdsourcing platform, and the results show that our framework outperforms state-of-the-art approaches on both cost and quality.
Proto-Chair: Posture-Sensing Smart Furniture with 3D-Printed Auxetics

Public space/furniture are amongst the new domains to apply a data-driven approach of design intervention and improvements. Open space is essentially dynamic, livable and interactive. Various types of people spend time for various purposes. Therefore, the "Measure-Test-Refine" loop is applicable for improving open spaces gradually.
The collection Virtual Machine

Getting the best performance from the ever-increasing number of hardware platforms has been a recurring challenge for data processing systems. In recent years, the advent of data science with its increasingly numerous and complex types of analytics has made this challenge even more difficult. In practice, system designers are overwhelmed by the number of combinations and typically implement a single analytics type on one platform, leading to repeated implementation effort---and a plethora of semi-compatible tools for data scientists.
Transition toward blockchain-based electricity trading markets

Nowadays, the technical feasibility and willingness to use blockchain-based electricity trading are taken for granted. However, in the not-too-distant past, the concept was no more than a mere vision. In this chapter, we investigate the sequence of events leading to this rapid paradigm shift over the past 10 years. The development stages of peer-to-peer electricity trading within the context of the fourth industrial revolution and the success of decentralized cryptocurrency markets are first presented. Afterward, the earliest applications of blockchain are demonstrated, along with key milestones in legislation and scientific research to reach the current situation.
Estimating Characteristic Sets for RDF Dataset Profiles Based on Sampling

RDF dataset profiles provide a formal representation of a dataset’s characteristics (features). These profiles may cover various aspects of the data represented in the dataset as well as statistical descriptors of the data distribution. In this work, we focus on the characteristic sets profile feature summarizing the characteristic sets contained in an RDF graph. As this type of feature provides detailed information on both the structure and semantics of RDF graphs, they can be very beneficial in query optimization. However, in decentralized query processing, computing them is challenging as it is difficult and/or costly to access and process all datasets. To overcome this shortcoming, we propose the concept of a profile feature estimation. We present sampling methods and projection functions to generate estimations which aim to be as similar as possible to the original characteristic sets profile feature. In our evaluation, we investigate the feasibility of the proposed methods on four RDF graphs. Our results show that samples containing $$0.5\%$$ of the entities in the graph allow for good estimations and may be used by downstream tasks such as query plan optimization in decentralized querying.
Noncommutative photon-added squeezed vacuum states

Noncommutative optical squeezed vacuum states are constructed as eigenstates of an appropriate two-photon annihilation operator corresponding to the Biedenharn–Macfarlane [Formula: see text]-oscillator. We consider in details the role of noncommutativity parameter [Formula: see text] on the nonclassical behaviors including quadrature squeezing and sub-Poissonian statistics. Also, we construct the noncommutative photon-added squeezed vacuum states and consider their Hillery-type higher-order squeezing and single-mode noise band.
Applying Radam Method to improve treatment of Convolutional Neural Network on Banknote Identification

With the denomination recognition of existing convolutional neural network methods, a method which is based on a Radam optimizer and improved LeNet neural network has been proposed to further addressing problems in the accuracy and efficiency of banknote recognition. In order to improve the training efficiency of the system, the method of transfer learning has been used to extract and recognize the stability and accuracy of input data. For denomination pictures of different sizes, the image normalization method is adopted to realize imported standardized neural network data. In order to further improve the robust performance of the system algorithm, data enhancement methods are introduced, including the geometric transformation of the original data, adding noise, brightness, etc. At end the network connects the fully connected layer and the softmax layer to output labels with different denominations. Experiments show that the network meets the system's real-time requirements, and the algorithm has greatly improved the stability and accuracy of RMB denomination recognition with improvement from 95% to 99.97%. The experimental results illustrate that the algorithm structure is very practical.
Advancement of the search process for digital heritage by utilizing artificial intelligence algorithms

The increasing amount of pressure to digitalize what we are used to consider a conventional data has created a need to analyze, search and process unique data structures in a timely manner. The progressive world has created a justified need not only for a fast query searches but also the most related and meaningful searches with a minimal guidance. This article explores the potential benefits of using unorthodox solutions, including Artificial Intelligence algorithms in processing big data that are unconventional data structures. One of such big data sources was created as part of digitalization of historic heritage materials, documents and artifacts. The article calls out the benefits and disadvantages of some Artificial Intelligence algorithms and explores ways to use a few of those algorithms for the purposes of the digital heritage. It also offers the solution to maximize the potential of the search engine that could be built for digital heritage or any other unstructured data.
Dual Extreme Learning Machines-Based Spatiotemporal Modeling for Nonlinear Distributed Thermal Processes

Many industrial thermal processes belong to distributed parameter systems (DPSs), which have two coupled nonlinear blocks. Dual least square support vector machines (LS-SVM) has been proposed to model such systems. However, due to the use of two LS-SVM, this method often leads to heavy computation and long learning time, which does not suit for online application. In this study, a dual extreme learning machine (ELM)-based spatiotemporal modeling method is proposed for such two nonlinearities embedded DPSs. Firstly, the KL method is applied to reduce the dimension of the original system and obtain the spatial basis functions (BFs). Then, dual ELM is designed to match the two nonlinear structures. Finally, through the reconstruction of space–time synthesis, the approximate spatiotemporal distribution model of the original system is obtained. In addition, simulations on a curing process is studied to confirm the effectiveness of the proposed method.
Designing Augmented Reality Applications as Learning Activity

This chapter examines the conceptual design of AR applications as a learning activity with low technical requirements. With the help of the learning activity, the principles of AR and of the subject area can be conveyed, and the learners’ engagement with the learning content should be stimulated. In this study, Bachelor students of civil engineering (N = 71) have been assigned the task of designing an AR application for the subject domain of technical infrastructures, such as traffic management or energy supply. The students describe the design of the AR application with an own photo shot of the technical infrastructure with the augmentation on the photo and an explanatory text of 100–200 words. Combined these three elements serve as an illustration, which is made available to fellow students in an online gallery for peer review by means of a questionnaire. The peer review is intended both to examine the quality of the contributions—and thus possible learning outcomes among the submitters—and to stimulate the reviewers to engage with AR and the subject domain. A subsequent survey evaluates the students' assessment of learning success. Although the study only suggests actual learning outcomes of the learning activity, it demonstrates how complex technologies, such as AR, can be integrated into learning contexts with low technical requirements.
Online Indices for Predictive Top-k Entity and Aggregate Queries on Knowledge Graphs

Knowledge graphs have seen increasingly broad applications. However, they are known to be incomplete. We define the notion of a virtual knowledge graph which extends a knowledge graph with predicted edges and their probabilities. We focus on two important types of queries: top-k entity queries and aggregate queries. To improve query processing efficiency, we propose an incremental index on top of low dimensional entity vectors transformed from network embedding vectors. We also devise query processing algorithms with the index. Moreover, we provide theoretical guarantees of accuracy, and conduct a systematic experimental evaluation. The experiments show that our approach is very efficient and effective. In particular, with the same or better accuracy guarantees, it is one to two orders of magnitude faster in query processing than the closest previous work which can only handle one relationship type.
WATouCH: Enabling Direct Input on Non-touchscreen Using Smartwatch's Photoplethysmogram and IMU Sensor Fusion

Interacting with non-touchscreens such as TV or public displays can be difficult and inefficient. We propose WATouCH, a novel method that localizes a smartwatch on a display and allows direct input by turning the smartwatch into a tangible controller. This low-cost solution leverages sensor fusion of the built-in inertial measurement unit (IMU) and photoplethysmogram (PPG) sensor on a smartwatch that is used for heart rate monitoring. Specifically, WATouCH tracks the smartwatch movement using IMU data and corrects its location error caused by drift using the PPG responses to a dynamic visual pattern on the display. We conducted a user study on two tasks -- a point and click and line tracing task -- to evaluate the system usability and user performance. Evaluation results suggested that our sensor fusion mechanism effectively confined IMU-based localization error, achieved encouraging targeting and tracing precision, was well received by the participants, and thus opens up new opportunities for interaction.