Reviewerly/exHarmony huggingface dataset:
Crossing-Based Selection with Virtual Reality Head-Mounted Displays

This paper presents the first investigation into using the goal-crossing paradigm for object selection with virtual reality (VR) head-mounted displays. Two experiments were carried out to evaluate ray-casting crossing tasks with target discs in 3D space and goal lines on 2D plane respectively in comparison to ray-casting pointing tasks. Five factors, i.e. task difficulty, the direction of movement constraint (collinear vs. orthogonal), the nature of the task (discrete vs. continuous), field of view of VR devices and target depth, were considered in both experiments. Our findings are: (1) crossing generally had shorter or no longer time, and higher or similar accuracy than pointing, indicating crossing can complement or substitute pointing; (2) crossing tasks can be well modelled with Fitts' Law; (3) crossing performance depended on target depth; (4) crossing target discs in 3D space differed from crossing goal lines on 2D plane in many aspects such as time and error performance, the effects of target depth and the parameters of Fitts' models. Based on these findings, we formulate a number of design recommendations for crossing-based interaction in VR.
Seeing with New Eyes

This paper presents the GIFT smartphone app, an artist-led Research through Design project benefitting from a three-day in-the-wild deployment. The app takes as its premise the generative potential of combining the contexts of gifting and museum visits. Visitors explore the museum, searching for objects that would most appeal to the gift-receiver they have in mind, then photographing those objects and adding audio messages for their receivers describing the motivation for their choices. This paper charts the designers' key aim of creating a new frame of mind using voice, and the most striking findings discovered during in-the-wild deployment in a museum -- 'seeing with new eyes' and fostering personal connections. We discuss empathy, motivation, and bottom-up personalisation in the productive space revealed by this combination of contexts. We suggest that this work reveals opportunities for designers of gifting services as well as those working in cultural heritage.
Supervised Robust Discrete Multimodal Hashing for Cross-Media Retrieval

The hashing-based approximate nearest neighbors search is able to reduce the storage cost and improve query speed. Therefore, they have attracted much attention in these years. Moreover, some hashing methods have been proposed for cross-modal retrieval tasks. However, there are still some issues that need to be further addressed. For example, some of them only construct a simple similarity matrix when learning hash functions or binary codes, which may lose some useful information. Some of them solve the hard discrete optimization problem by relaxing the binary constraints and quantizing the solution to obtain the final results, which may generate large quantization errors. To address these challenges, we present a new supervised cross-modal hashing method, named supervised robust discrete multimodal hashing (SRDMH). Specifically, it incorporates full label information into the hash functions learning to preserve the similarity in the original space. In addition, instead of relaxing the binary constraints, it is able to learn the binary codes and hash functions simultaneously. Moreover, it adopts a flexible ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,p</sub> loss with nonlinear kernel embedding and introduces an intermediate presentation of the binary codes. In light of this, it becomes more robust and easier to solve by an iterative algorithm presented in this paper. To evaluate its performance, we conduct extensive experiments on three benchmark datasets. The results verify that SRDMH outperforms seven state-of-the-art cross-modal hashing methods. In addition, we also extend it to the classification task. Compared with other hashing methods, SRDMH also obtains better results when its binary codes are used for classification.
Knowledge Graph-Based Image Classification Refinement

Biologically inspired ideas are important in image processing.Not only does more than 80% of the information received by humans comes from the visual system, but the human visual system also gives its fast, accurate, and efficient image processing capability.In the current image classification task, convolutional neural networks (CNNs) focus on processing pixels and often ignore the semantic relationships and human brain mechanisms.With the development of image analysis and processing techniques, the information in the image is becoming increasingly complicated.Humans can learn about the characteristics of objects and the relationships that occur between them to classify the images.It is a significant characteristic that sets humans apart from the modern learning-based computer vision algorithms.How to make full use of the semantic relationships in categories and how to apply the knowledge of biological vision to image classification are our main concerns.In this view, we propose the concept of the image knowledge graph (IKG) to incorporate the semantic association and the scene association to fully consider the relations between objects (external and internal).We take full advantage of the reasoning model of the knowledge graph that is closer to the biological visual information-processing model.We conduct extensive experiments on large-scale image datasets (ImageNet), demonstrating the effectiveness of our approach.Furthermore, our method participates in ILSVRC 2017 challenges and obtains the new state-of-the-art results on the ImageNet (82.43%).
"My blood sugar is higher on the weekends"

Tools for self-care of chronic conditions often do not fit the contexts in which self-care happens because the influence of context on self-care practices is unclear. We conducted a diary study with 15 adolescents with Type 1 Diabetes and their caregivers to understand how context affects self-care. We observed different contextual settings, which we call contextual frames, in which diabetes self-management varied depending on certain factors - physical activity, food, emotional state, insulin, people, and attitudes. The relative prevalence of these factors across contextual frames impacts self-care necessitating different types of support. We show that contextual frames, as phenomenological abstractions of context, can help designers of context-aware systems systematically explore and model the relation of context with behavior and with technology supporting behavior. Lastly, considering contextual frames as sensitizing concepts, we provide design direction for using context in technology design.
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning

Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we propose an efficient algorithm for graph partitioning, the "Mutex Watershed''. Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. We also prove that this simple algorithm solves to global optimality an objective function that is intimately related to the multicut / correlation clustering integer linear programming formulation. The algorithm is deterministic, very simple to implement, and has empirically linearithmic complexity. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives the best results currently known for the competitive ISBI 2012 EM segmentation benchmark.
A Self-attention Based LSTM Network for Text Classification

Neural networks have been used to achieve impressive performance in Natural Language Processing (NLP). Among all algorithms, RNN is a widely used architecture for text classification tasks. The main challenge in sentiment classification is the quantification of the connections between context words in a sentence. Even though various types and structures of model have been proposed, they encounter the problem of gradient vanishing and are unlikely to show the full potential of the network. In this work, we present a new RNN model based on the self-attention mechanism to improve the performance while dealing with long sentences and whole documents. Empirical results show that our model outperforms the state-of-art algorithms.
High-Precision Measurement of Binocular Telecentric Vision System With Novel Calibration and Matching Methods

In stereo vision-based three-dimensional measurements, calibration, and stereo matching are the most challenging tasks for accurate three-dimensional reconstruction.The traditional binocular vision algorithm has low precision, and we propose a binocular vision system using a telecentric lens.In this paper, we propose a calibration and matching algorithm for the telecentric binocular vision system, which collects only two pictures of the calibration plate to complete the system calibration.The algorithm is a special application of 3D reconstruction.In this paper, if the measurement points are selected as points on the grid, the appropriate extension can be used for high-precision 3D reconstruction, which will be explained in detail in the experiments.In order to reduce the calibration error and obtain high maneuverability, the binocular vision system is only calibrated once with a two-dimensional calibration board, and the depth information is obtained through the parallax of the detection process, so as to effectively simplify the calibration process and reduce the errors introduced in the calibration process.For the matching of images in the right and left cameras, the polar theory of telecentric binocular vision system is constructed according to the characteristics of telecentric lens imaging model based on the traditional binocular vision system theory.The matching algorithm only needs to apply the two-dimensional calibration information and then calculates the results according to the parallax combined with the polar line matching algorithm, which simplifies the algorithm flow and reduces the error.In this paper, experiments show that the algorithm proposed is simple and has high precision, good stability, and high-practical value.
Top-N group recommendations with fairness

In many settings it is required that items are recommended to a group of users instead of a single user. Conventionally, the objective is to maximize the overall satisfaction among group members. Recently, however, attention has shifted to ensuring that recommendations are fair in that they should minimize the feeling of dissatisfaction among members. In this work, we explore a simple but intuitive notion of fairness: the minimum utility a group member receives. We propose a technique that seeks to rank the Pareto, or unanimously, optimal items by considering all admissible ways in which a group might reach a decision. As our detailed experimental study shows, this results in top-N recommendations that not only achieve a high minimum utility compared to other fairness-aware techniques, but also a high average utility across all group members beating standard aggregation strategies.
Multimodal Multimedia Retrieval with vitrivr

The steady growth of multimedia collections - both in terms of size and heterogeneity - necessitates systems that are able to conjointly deal with several types of media as well as large volumes of data. This is especially true when it comes to satisfying a particular information need, i.e., retrieving a particular object of interest from a large collection. Nevertheless, existing multimedia management and retrieval systems are mostly organized in silos and treat different media types separately. Hence, they are limited when it comes to crossing these silos for accessing objects. In this paper, we present vitrivr, a general-purpose content-based multimedia retrieval stack. In addition to the keyword search provided by most media management systems, vitrivr also exploits the object's content in order to facilitate different types of similarity search. This can be done within and, most importantly, across different media types giving rise to new, interesting use cases. To the best of our knowledge, the full vitrivr stack is unique in that it seamlessly integrates support for four different types of media, namely images, audio, videos, and 3D models.
Interpretable multiclass classification by MDL-based rule lists

Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts. Examples of interpretable classification models include decision trees, rule sets, and rule lists. Learning such models often involves optimizing hyperparameters, which typically requires substantial amounts of data and may result in relatively large models. In this paper, we consider the problem of learning compact yet accurate probabilistic rule lists for multiclass classification. Specifically, we propose a novel formalization based on probabilistic rule lists and the minimum description length (MDL) principle. This results in virtually parameter-free model selection that naturally allows to trade-off model complexity with goodness of fit, by which overfitting and the need for hyperparameter tuning are effectively avoided. Finally, we introduce the Classy algorithm, which greedily finds rule lists according to the proposed criterion. We empirically demonstrate that Classy selects small probabilistic rule lists that outperform state-of-the-art classifiers when it comes to the combination of predictive performance and interpretability. We show that Classy is insensitive to its only parameter, i.e., the candidate set, and that compression on the training set correlates with classification performance, validating our MDL-based selection criterion.
A Blockchain-based and resource-aware process execution engine

Business Process Management provides concepts for enterprises to design, document and execute their business processes. Involved artefacts establish the basis for a profound enhancement of processes, like performance improvements or a substantiate assignment of employees (resources) to tasks. These artefacts comprise process models for documentation purposes or Workflow Management Systems for executing processes. Those concepts are adopted well within single companies but are barely implemented for inter-organizational processes reasoned in a lack of trust between participating business partners. For instance, questions like who the host of the workflow engine is and thus have sovereignty over the process execution remains open. Besides expensive third parties, we believe that Blockchain Technology bears potential to solve the lack of trust due to automated consensus finding. We present a Blockchain-based process engine guaranteeing a tamper-proof execution, which is both, highly scalable regarding the size of process models as well as capable of considering employees captured in the organizational perspective of the process model. Comparable approaches suffer from lack of trustworthiness either, are not applicable for large process models, or do not include organizational concerns.
Adversarial cross-modal retrieval based on dictionary learning

Existing cross-modal approaches focus on learning a subspace or using classical neural networks for similarity measurement of different modalities, which ignore the complex statistical properties of multimodal data. To settle the above problems, we propose a novel framework termed Adversarial Cross-Modal Retrieval Based on Dictionary Learning Algorithm (DLA-CMR). The dictionary learning serves as feature reconstructor to reconstruct discriminative features, while adversarial learning mines the statistical characteristics for each modality. Firstly, using all of the training (testing) samples to reconstruct each training (testing) sample, the specificity of each sample is maintained to some extent. Secondly, the weight of important features increases while that of secondary features decreases. This also makes the dimension of transformed visual modality approximate to textual modality. In addition, the adversarial learning guarantees that the transformed features maintain the inherent statistical characteristics of original features for each modality, and it requires transformed features to be statistically indistinguishable in common space. The transformed features must be maximally correlated to eliminate the heterogeneous gap. Comprehensive experimental results compared with 7 state-of-the-art methods on 4 widely-used datasets verify the effectiveness of our DLA-CMR method.
Study on intelligent collision avoidance and recovery path planning system for the waterjet-propelled unmanned surface vehicle

To avoid collision accidents and achieve independent recovery in the cluttered marine environment, this work proposes an intelligent path planning system for a waterjet-propelled unmanned surface vehicle (USV). Unlike the existing works on USV navigation systems, our study focuses on the real-time, smoothness and seaworthiness properties of the path in practical clutter environments. A hybrid A* algorithm with motion primitive constraints is proposed to generate an initial reference path. According to different types of dynamic obstacles, the International Regulations for Preventing Collisions at Sea (COLREGs) rules are used, and a local threat map based on the Apollonius circle is constructed to avoid these obstacles. Together with the motion characteristics of the waterjet-propelled USV, the Reeds-Shepp curve is used to calculate the autonomous recovery path. The performance of the proposed intelligent navigation system is verified in a practical marine environment. The experimental results show that our hybrid A* algorithm outperforms the conventional A* algorithm, and it is easy for the waterjet-propelled USV to follow the final continuous curvature path. It was demonstrated that our method can effectively avoid various static and dynamic obstacles in real time and thus help achieve autonomous recovery.
Reportcoin: A Novel Blockchain-Based Incentive Anonymous Reporting System

With the widespread popularity of Internet-enabled devices, mobile users can request and receive messages anytime and anywhere, which facilitates information feedback for smart city management.However, few people are willing to reflect or report some violations of law and discipline around them, and more people choose to ignore.In general, there are two major reasons for this phenomenon.First, reporting with a real name is highly recommended, but it is difficult to send trusted and reliable reporting messages without revealing the reporter's identity.Second, generally no benefit, users usually lack the motivation to report due to worrying about being retaliated.In this paper, we propose an effective anonymous reporting system called ReportCoin, a novel Blockchain-based incentive anonymous reporting system.ReportCoin guarantees user identity privacy and reporting message reliability throughout the reporting process.On the one hand, ReportCoin allows nondeterministic mobile users to vote the reporting by signing and to send anonymous announcements in the non-fully trusted network.On the other hand, ReportCoin motivates users with incentives to report without worrying about the disclosure of identity information to be retaliated.Meanwhile, account information and transaction records in ReportCoin are open, transparent, and tamperresistant.The theoretical analysis and extensive experimental results show that ReportCoin is efficient and practical.
Using a Multi-Task Recurrent Neural Network With Attention Mechanisms to Predict Hospital Mortality of Patients

Estimating hospital mortality of patients is important in assisting clinicians to make decisions and hospital providers to allocate resources. This paper proposed a multi-task recurrent neural network with attention mechanisms to predict patients' hospital mortality, using reconstruction of patients' physiological time series as an auxiliary task. Experiments were conducted on a large public electronic health record database, i.e., MIMIC-III. Fifteen physiological measurements during the first 24 h of critical care were used to predict death before hospital discharge. Compared with the conventional simplified acute physiology score (SAPS-II), the proposed multi-task learning model achieved better sensitivity (0.503 ± 0.020 versus 0.365 ± 0.021), when predictions were made based on the same 24-h observation period. The multi-task learning model is recommended to be updated daily with at least a 6-h observation period, in order for it to perform similarly or better than the SAPS-II. In the future, the need for intervention can be considered as another task to further optimize the performance of the multi-task learning model.
Matrix-Vector vs. Matrix-Matrix Multiplication: Potential in DD-based Simulation of Quantum Computations

The simulation of quantum computations basically boils down to the multiplication of vectors (describing the respective quantum state) and matrices (describing the respective quantum operations). However, since those matrices/vectors are exponential in size, most of the existing solutions (relying on arrays for their representation) are either limited to rather small quantum systems or require substantial hardware resources. To overcome these shortcomings, solutions based on decision diagrams (DD-based simulation) have been proposed recently. They exploit redundancies in quantum states as well as matrices and, by this, allow for a compact representation and manipulation. This offers further (unexpected) potential. In fact, simulation has been conducted thus far by applying one operation (i.e. one matrix-vector multiplication) after another. Besides that, there is the possibility to combine several operations (requiring a matrix-matrix multiplication) before applying them to a vector. But since, from a theoretical perspective, matrix-vector multiplication is significantly cheaper than matrix-matrix multiplication, the potential of this direction was rather limited thus far. In this work, we show that this changes when decision diagrams are employed. In fact, their more compact representation frequently makes matrix-matrix multiplication more beneficial—leading to substantial improvements by exploiting the combination of operations. Experimental results confirm the proposed strategies for combining operations lead to speed-ups of several factors or—when additionally exploiting further knowledge about the considered instance—even of several orders of magnitudes.
A Quantum Particle Swarm Optimizer With Enhanced Strategy for Global Optimization of Electromagnetic Devices

Quantum particle swarm optimization (QPSO), inspired from the basic concept of PSO algorithm and quantum theory, is a stochastic searching algorithm. However, the algorithm may encounter a premature convergence when dealing with multimodal and complex inverse problems. Thus, some improvements are introduced. More especially, one will randomly select the best particle to take part in the current search domain. Also, a mutation strategy is added to the mean best position, and an enhancement factor (EF) is incorporated to enhance the global search capability to find the global optimum solution and to avoid premature convergence. Moreover, some parameter updating strategy is proposed to tradeoff the exploration and exploitation searches. Experiments have been conducted on well-known multimodal functions and an inverse problem. The numerical results showcase the merit and efficiency of the proposed modified quantum inspired particle swarm optimizer (MQPSO).