Reviewerly/exHarmony huggingface dataset:
Image-Only Real-Time Incremental UAV Image Mosaic for Multi-Strip Flight

Limited by aircraft flight altitude and camera parameters, it is necessary to obtain wide-angle panoramas quickly by stitching aerial images, which is helpful in rapid disaster investigation, recovery after earthquakes, and aerial reconnaissance. However, most existing stitching algorithms do not simultaneously meet practical real-time, robustness, and accuracy requirements, especially in the case of a long-distance multistrip flight. In this paper, we propose a novel image-only real-time UAV image mosaic framework for long-distance multistrip flights that does not require any auxiliary information, such as GPS or GCPs. The framework has a complete structure, mainly consisting of the three tasks of automatic initialization, current frame tracking, and real-time mosaic generation. The stitching plane is determined in the initialization process, the homography transformation of the current image is estimated in the tracking task, and the image is mapped to the stitching plane to generate and update the panorama in the real-time mosaic process. The core idea is that, in the tracking task, we introduce and develop a keyframe insertion strategy to generate a keyframe list and, on this basis, design a homography matrix estimation based on a local optimization strategy to reduce the accumulated error when continuously stitching image sequences collected online by UAVs and to realize real-time, effective UAV image mosaic construction. In addition, this framework has good scalability, which is not limited to a specific algorithm. To evaluate the effectiveness of the proposed framework, we carry out a large number of experiments on the AirSim simulation platform and present an exhaustive evaluation in some sequences from a popular dataset. Qualitative and quantitative experimental results in simulation and real environments demonstrate that our algorithm can obtain an effective and robust mosaic image in real-time. Through strategy comparison experiments, it is proven that the keyframe insertion strategy and the local optimization strategy both improve the stitching performance. Compared with five state-of-art image stitching approaches, the mosaic effect of the proposed method is comparable or better. In terms of algorithm speed, its performance is superior to them. Additionally, experiments of illumination change and feature replacement in the framework verify the good adaptability and scalability of the algorithm.
A Framework for Deep Constrained Clustering - Algorithms and Advances

The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations. A fundamental strength of deep learning is its flexibility, and here we explore a deep learning framework for constrained clustering and in particular explore how it can extend the field of constrained clustering. We show that our framework can not only handle standard together/apart constraints (without the well documented negative effects reported earlier) generated from labeled side information but more complex constraints generated from new types of side information such as continuous values and high-level domain knowledge. (Source code available at: http://github.com/blueocean92/deep_constrained_clustering )
Efficient feature extraction model for validation performance improvement of duplicate bug report detection in software bug triage systems

There are many duplicate bug reports in the semi-structured software repository of various software bug triage systems. The duplicate bug report detection (DBRD) process is a significant problem in software triage systems. The DBRD problem has many issues, such as efficient feature extraction to calculate similarities between bug reports accurately, building a high-performance duplicate detector model, and handling continuous real-time queries. Feature extraction is a technique that converts unstructured data to structured data. The main objective of this study is to improve the validation performance of DBRD using a feature extraction model. This research focuses on feature extraction to build a new general model containing all types of features. Moreover, it introduces a new feature extractor method to describe a new viewpoint of similarity between texts. The proposed method introduces new textual features based on the aggregation of term frequency and inverse document frequency of text fields of bug reports in uni-gram and bi-gram forms. Further, a new hybrid measurement metric is proposed for detecting efficient features, whereby it is used to evaluate the efficiency of all features, including the proposed ones. The validation performance of DBRD was compared for the proposed features and state-of-the-art features. To show the effectiveness of our model, we applied it and other related studies to DBRD of the Android, Eclipse, Mozilla, and Open Office datasets and compared the results. The comparisons showed that our proposed model achieved (i) approximately 2% improvement for accuracy and precision and more than 4.5% and 5.9% improvement for recall and F1-measure, respectively, by applying the linear regression (LR) and decision tree (DT) classifiers and (ii) a performance of 91%−99% (average ~97%) for the four metrics, by applying the DT classifier as the best classifier. Our proposed features improved the validation performance of DBRD concerning runtime performance. The pre-processing methods (primarily stemming) could improve the validation performance of DBRD slightly (up to 0.3%), but rule-based machine learning algorithms are more useful for the DBRD problem. The results showed that our proposed model is more effective both for the datasets for which state-of-the-art approaches were effective (i.e., Mozilla Firefox) and those for which state-of-the-art approaches were less effective (i.e., Android). The results also showed that the combination of all types of features could improve the validation performance of DBRD even for the LR classifier with less validation performance, which can be implemented easily for software bug triage systems. Without using the longest common subsequence (LCS) feature, which is effective but time-consuming, our proposed features could cover the effectiveness of LCS with lower time-complexity and runtime overhead. In addition, a statistical analysis shows that the results are reliable and can be generalized to other datasets or similar classifiers.
PushdownDB: Accelerating a DBMS Using S3 Computation

This paper studies the effectiveness of pushing parts of DBMS analytics queries into the Simple Storage Service (S3) of Amazon Web Services (AWS), using a recently released capability called S3 Select. We show that some DBMS primitives (filter, projection, and aggregation) can always be cost-effectively moved into S3. Other more complex operations (join, top-K, and group-by) require reimplementation to take advantage of S3 Select and are often candidates for pushdown. We demonstrate these capabilities through experimentation using a new DBMS that we developed, PushdownDB. Experimentation with a collection of queries including TPC-H queries shows that PushdownDB is on average 30% cheaper and 6.7× faster than a baseline that does not use S3 Select.
ARTEMIS: Mixed-Reality Environment for Immersive Surgical Telementoring

The golden hour following a traumatic injury holds the highest likelihood where surgical treatment may prevent mortality and morbidity. However, the increasing occurrence of large-scale disasters, overwhelm local medical systems and patients find themselves without timely access to medical expertise. To respond to this problem medical care experts are exploring telemedicine, which typically attempts to use synchronous audiovisual communication. While current telemedicine approaches have limited ability to impact the physical care required for trauma, AR technology allows medical professionals to see their patients while seeing additional digital information. In this paper we describe ARTEMIS (Augmented Reality Technology to Enable reMote Integrated Surgery), an immersive AR-VR telementoring infrastructure that allows experienced surgeons to remotely aid less experienced medical professionals in the field. ARTEMIS provides Mixed Reality immersive visual aids by tracking a patient in real-time and showing a reconstructed 3D point cloud in a VR environment; expert surgeons can interact with the 3D point cloud representation of the patient, instruct the remote novice through real-time 3D annotations projected in AR on the patient's body, using hand-maneuvers shown in AR through an avatar of the expert surgeon, and by projecting small video clips of specific procedures in the AR space for the novice to follow.
Sensor Illumination: Exploring Design Qualities and Ethical Implications of Smart Cameras and Image/Video Analytics

Drawing analogies between smart cameras and electric lighting, we highlight and extrapolate design trends towards always-on sensing in intimate contexts, and the functional expansion of smart cameras as general-purpose and multi-functional devices. Employing a research through design (RtD) approach, we extrapolate these trends using speculative scenarios, materialize the scenarios by designing and constructing lighting-inspired smart camera fixtures, and self-experiment with these fixtures to introduce and exacerbate privacy and security issues, and inspire creative workarounds and design opportunities for sensor-level regulation. We synthesize our insights by presenting 8 smart camera sensing design qualities for addressing privacy, security, and related social and ethical issues.
Automated Design of Neural Network Architectures With Reinforcement Learning for Detection of Global Manipulations

Deep Convolutional Neural Networks (DCNNs) have been widely used in detection of global manipulations. However, designing effective DCNNs for specific image forensics tasks generally requires domain knowledge and experience gained from abundant experiments, which is time-consuming and labor-expensive. Approaches of automated network designing have been proposed for image classification tasks which are image-content focused, however they may not be suitable to image forensics tasks which rely on identifying subtle traces left by certain image operations. In this paper, we make the first attempt to automate the neural network architecture design for detection of global manipulations. The process of constructing a network is modeled as sequentially selecting optimal architecture modules to generate high-performing CNNs for specific forensic tasks through reinforcement learning. The module-based search space is proposed to make the designing process efficient. Advanced connection patterns (e.g., dense connectivity), which were shown preferred for global manipulation detections, are included in the modules to improve the representational power of the network. Experimental results show that the proposed approach can adaptively construct effective CNN architectures for two common forensic tasks, including multi-purpose forensics and the processing history detection. The auto-designed networks can outperform the state-of-the-art manually designed networks.
Empirical evaluation of Linked Data visualization tools

The economic impact of open data in Europe has an estimated value of € 140 billions a year between direct and indirect effects. The social impact is also known to be high, as the use of more transparent open data have been enhancing public services and creating new opportunities for citizens and organizations. We are assisting at a staggering growth in the production and consumption of Linked Data (LD). Exploring, visualizing and analyzing LD is a core task for a variety of users in numerous scenarios. This paper deeply analyzes the state of the art of tools for LD visualization. Linked Data visualization aims to provide graphical representations of datasets or of some information of interest selected by a user, with the aim to facilitate their analysis. A complete list of 77 LD visualization tools has been created starting from tools listed in previous surveys or research papers and integrating newer tools recently published online. The visualization tools have been described and compared based on their usability, and their features. A set of goals that LD tools should implement in order to provide clear and convincing visualizations has been defined and 14 tools have been tested on a big LD dataset. The results of this comparison and test led us to define some suggestions for LD consumers in order for them to be able to select the most appropriate tools based on the type of analysis they wish to perform.
Nailz: Sensing Hand Input with Touch Sensitive Nails

Touches between the fingers of an unencumbered hand represent a ready-to-use, eyes-free and expressive input space suitable for interacting with wearable devices such as smart glasses or watches. While prior work has focused on touches to the inner surface of the hand, touches to the nails, a practical site for mounting sensing hardware, have been comparatively overlooked. We extend prior implementations of single touch sensing nails to a full set of five and explore their potential for wearable input. We present design ideas and an input space of 144 touches (taps, flicks and swipes) derived from an ideation workshop. We complement this with data from two studies characterizing the subjective comfort and objective characteristics (task time, accuracy) of each touch. We conclude by synthesizing this material into a set of 29 viable nail touches, assessing their performance in a final study and illustrating how they could be used by presenting, and qualitatively evaluating, two example applications.
A neural knowledge graph evaluator: Combining structural and semantic evidence of knowledge graphs for predicting supportive knowledge in scientific QA

Effectively detecting supportive knowledge of answers is a fundamental step towards automated question answering. While pre-trained semantic vectors for texts have enabled semantic computation for background-answer pairs, they are limited in representing structured knowledge relevant for question answering. Recent studies have shown interests in enrolling structured knowledge graphs for text processing, however, their focus was more on semantics than on graph structure. This study, by contrast, takes a special interest in exploring the structural patterns of knowledge graphs. Inspired by human cognitive processes, we propose novel methods of feature extraction for capturing the local and global structural information of knowledge graphs. These features not only exhibit good indicative power, but can also facilitate text analysis with explainable meanings. Moreover, aiming to better combine structural and semantic evidence for prediction, we propose a Neural Knowledge Graph Evaluator (NKGE) which showed superior performance over existing methods. Our contributions include a novel set of interpretable structural features and the effective NKGE for compatibility evaluation between knowledge graphs. The methods of feature extraction and the structural patterns indicated by the features may also provide insights for related studies in computational modeling and processing of knowledge.
Evolutionary Algorithm with a Configurable Search Mechanism

Abstract In this paper, we propose a new population-based evolutionary algorithm that automatically configures the used search mechanism during its operation, which consists in choosing for each individual of the population a single evolutionary operator from the pool. The pool of operators comes from various evolutionary algorithms. With this idea, a flexible balance between exploration and exploitation of the problem domain can be achieved. The approach proposed in this paper might offer an inspirational alternative in creating evolutionary algorithms and their modifications. Moreover, different strategies for mutating those parts of individuals that encode the used search operators are also taken into account. The effectiveness of the proposed algorithm has been tested using typical benchmarks used to test evolutionary algorithms.
Designing in the Network of Relations for Species Conservation: The Playful Tingtibi Community Birdhouse

This paper investigates connecting people in remote communities through nature in order to foster stewardship and conservation of endangered species. Global citizen science technologies have found success in urban, developed countries, but they typically rely on large distributed populations to gather or analyze data and do not suit sparsely populated and remote contexts. We undertook a long-term field study to iteratively co-design a tangible and playful nature engagement prototype in a remote World Heritage Area community. The prototype design fosters learning through ambient sounds as well as exploration and discovery of species through nature soundscape recordings. We found that the prototypes amplified locals' interest, became embedded in community relations and gradually led to placemaking of new engagement 'spaces' and of newer forms. We contribute lessons learned on how design can foster nature engagement and stewardship of endangered species by heeding Suchman's call for design to "enter networks of relations that make technology possible". We contribute design implications and new design foci HCI/Citizen science engagement for species conservation.
A new NTRU cryptosystem outperforms three highly secured NTRU-analog systems through an innovational algebraic structure

NTRU public key cryptosystem relies on computations that are efficiently performed with insignificant storage and time complexity. Many researchers were motivated to improve NTRU performance by replacing the original polynomial ring and mathematical structure with new modified algebraic structures, such as QTRU, OTRU, BITRU, and many others. In this work, a new NTRU-analog cryptosystem called QOBTRU is proposed based on a newly designed algebraic structure called Carternion algebra. QOBTRU is at least quadratic higher than the original NTRU in terms of computational and space complexity. It is intended to have an alternative security and performance attribute upon three chosen highly performed multidimensional NTRU-like cryptosystems, which are QTRU, OTRU, and BITRU. Their comparison with QOBTRU highlighted its strength over them. Some statistical and security analysis is performed to demonstrate its efficiency.
Enabling Efficient Random Access to Hierarchically-Compressed Data

Recent studies have shown the promise of direct data processing on hierarchically-compressed text documents. By removing the need for decompressing data, the direct data processing technique brings large savings in both time and space. However, its benefits have been limited to data traversal operations; for random accesses, direct data processing is several times slower than the state-of-the-art baselines. This paper presents a set of techniques that successfully eliminate the limitation, and for the first time, establishes the feasibility of effectively handling both data traversal operations and random data accesses on hierarchically-compressed data. The work yields a new library, which achieves 3.1× speedup over the state-of-the-art on random data accesses to compressed data, while preserving the capability of supporting traversal operations efficiently and providing large (3.9×) space savings.
Technology for Situated and Emergent Play: A Bridging Concept and Design Agenda

Despite the capacity of play to spontaneously emerge in our daily life, the scope of application of play design in HCI is generally narrower, specifically targeting areas of pure leisure, or wholly utilitarian and productive play. Here we focus on the value of play design to respond to and support our natural gravitation towards emergent play that helps to meet our social and emotional needs. We present a bridging concept: Technology for Situated and Emergent Play, i.e. technology design that supports playful engagement that emerges interwoven with our everyday activities outside leisure, and that enriches these activities with socio-emotional value. Our intermediate-level contribution has value as a synthesis piece: it weaves together theories of play and play design and bridges them with concrete design examples. As a bridging concept, it contributes: i) theoretical grounding; ii) inspiring design exemplars that illustrate the theory and foreground its value; and iii) design articulations in the form of valuable experiential qualities and design features. Our work can help to focus design agendas for playful technology and inspire future designs in this space.
Integration of remote sensing, gravity and geochemical data for exploration of Cu-mineralization in Alwar basin, Rajasthan, India

In spite of the dominance of traditional mineral exploration methods that demand physical characterization of rocks and intense field work, remote sensing technologies have also evolved in the recent past to facilitate mineral exploration. In the present study, we have processed visible near infrared (VNIR) and shortwave infrared (SWIR) bands of Advanced space-borne thermal emission and reflection radiometer (ASTER) data to detect surface mineralization signatures in Mundiyawas - Khera area in Alwar basin, north-eastern Rajasthan, India using spectral angle mapper (SAM). The potential of SAM method to detect target under variable illumination condition was used to delineate galena, chalcopyrite, malachite etc. as surface signatures of mineralization. It was ensured that the identified surface anomalies were spectrally pure using pixel purity index. Spectral anomalies were validated in the field and also using X-Ray diffraction data. Spectral anomaly maps thus derived were integrated using weight of evidence method with the lineament density, geochemical anomaly, bouger anomaly maps to identify few additional potential areas of mineralization. This study thus establishes the importance of remote sensing in mineral exploration to zero in on potentially ore rich but unexplored zones.
Reminding child cyclists about safety gestures

Cycling safety gestures, such as hand signals and shoulder checks, are an essential part of safe manoeuvring on the road. Child cyclists, in particular, might have difficulties performing safety gestures on the road or even forget about them, given the lack of cycling experience, road distractions and differences in motor and perceptual-motor abilities compared with adults. To support them, we designed two methods to remind about safety gestures while cycling. The first method employs an icon-based reminder in heads-up display (HUD) glasses and the second combines vibration on the handlebar and ambient light in the helmet. We investigated the performance of both methods in a controlled test-track experiment with 18 children using a mid-size tricycle, augmented with a set of sensors to recognize children's behavior in real time. We found that both systems are successful in reminding children about safety gestures and have their unique advantages and disadvantages.