Reviewerly/exHarmony huggingface dataset:
Enabling immersive engagement in energy system models with deep learning

Abstract Complex ensembles of energy simulation models have become significant components of renewable energy research in recent years. Often the significant computational cost, high‐dimensional structure, and other complexities hinder researchers from fully utilizing these data sources for knowledge building. Researchers at National Renewable Energy Laboratory have developed an immersive visualization workflow to dramatically improve user engagement and analysis capability through a combination of low‐dimensional structure analysis, deep learning, and custom visualization methods. We present case studies for two energy simulation platforms.
pRide

Promising unprecedented convenience, Online Ride Hailing (ORH) service such as Uber and Didi has gained increasing popularity. Different from traditional taxi service, this new on-demand transportation service allows users to request rides from the online service providers at the touch of their fingers. Despite such great convenience, existing ORH systems require the users to expose their locations when requesting rides - a severe privacy issue in the face of untrusted or compromised service providers. In this paper, we propose a private yet efficient ride request scheme, allowing the user to enjoy public ORH service without sacrificing privacy. Unlike previous works, we consider a more practical setting where the information about the drivers and road networks is public. This poses an open challenge to achieve strong security and high efficiency for the secure ORH service. Our main leverage in addressing this problem is hardware-enforced Trusted Execution Environment, in particular Intel SGX enclave. However, the use of secure enclave does not lead to an immediate solution due to the hardware's inherent resource constraint and security limitation. To tackle the limited enclave space, we first design an efficient ride-matching algorithm utilizing hub-based labeling technique, which avoids loading massive road network data into enclave during online processing. To defend against side-channel attacks, we take the next step to make the ride-matching algorithm data-oblivious, by augmenting it with oblivious label access and oblivious distance computation. The proposed solution provides high efficiency of real-time response and strong security guarantee of data-obliviousness. We implement a prototype system of the proposed scheme and thoroughly evaluate it from both theoretical and experimental aspects. The results show that the proposed scheme permits accurate and real-time ride-matching with provable security.
Trends in digitalization of education and training for industry 4.0 in the Russian Federation

In the paper, the authors consider the development trends in digitalization of education and training for industry 4.0 in the Russian Federation. The widespread use of digital technology in education is driven by specific new goals and objectives, new internal resources of the country (including experience), shortcomings in the forms and structure of employment, threats in the demographic sphere and other challenges of the external environment. An analysis of the mathematical model the use of digital technology in education of the distance learning led to the conclusion that, the success of training depends on individual properties of the trainee which are quantitatively estimated by a system indicator of quality of training.
Evaluating the Flexibility of A* for Mapping Quantum Circuits

Mapping quantum circuits to real quantum architectures (while keeping the respectively considered cost as small as possible) has become an important research task since it is required to execute algorithms on real devices. Since the underlying problem is NP-complete, several heuristic approaches have been proposed. Recently, approaches utilizing A $$^*$$ search to map quantum circuits to, e.g., Nearest Neighbor architectures or IBM QX architectures have gained substantial interest. However, their performance usually has only been evaluated in a rather narrow context, i.e., for single architectures and objectives only. In this work, we evaluate the flexibility of A $$^*$$ in the context of mapping quantum circuits to physical devices. To this end, we review the underlying concepts and show its flexibility with respect to the considered architecture. Furthermore, we demonstrate how easy such solutions can be adjusted towards optimizing different design objectives or cost metrics by providing a generalized and parameterizable cost function for the A $$^*$$ search that can also be easily extended to support future cost metrics.
Adaptive sparse graph learning based dimensionality reduction for classification

To preserve the sparsity structure in dimensionality reduction, sparsity preserving projection (SPP) is widely used in many fields of classification, which has the advantages of noise robustness and data adaptivity compared with other graph based method. However, the sparsity parameter of SPP is fixed for all samples without any adjustment. In this paper, an improved SPP method is proposed, which has an adaptive parameter adjustment strategy during sparse graph construction. With this adjustment strategy, the sparsity parameter of each sample is adjusted adaptively according to the relationship of those samples with nonzero sparse representation coefficients, by which the discriminant information of graph is enhanced. With the same expectation, similarity information both in original space and projection space is applied for sparse representation as guidance information. Besides, a new measurement is introduced to control the influence of each sample’s local structure on projection learning, by which more correct discriminant information should be preserved in the projection space. With the contributions of above strategies, the low-dimensional space with high discriminant ability is found, which is more beneficial for classification. Experimental results on three datasets demonstrate that the proposed approach can achieve better classification performance over some available state-of-the-art approaches.
Assist Users' Interactions in Font Search with Unexpected but Useful Concepts Generated by Multimodal Learning

When searching for suitable fonts for a digital graphic, users usually start with an ambiguous thought. For example, they would look for fonts that are suitable for a personal web page or party invitations for children. Their design concept becomes clearer as they interact with external interventions such as exposure to suitable images for use in their web page or the children's preferences regarding the party. Hence, it is important to support users' interactions with unexpected but useful concepts during their search. In this paper, we present a novel framework that helps users to explore a font dataset using the multimodal method that provides unexpected but useful font images or concept words in response to the user's input. We collect a large font dataset and the associated tags and propose the use of unsupervised generative model that jointly learns the correlation between the visual features of a font and the associated tags for the creative process. By examining the results of the model that change with various inputs, we observed that the model produces highly promising results. In the experiment, we verified that the generated concepts by the model are not only new but also relevant to the user input that appears to be useful for inspiring users.
Joins Over Encrypted Data with Fine Granular Security

Performing joins over encrypted data is particularly challenging, since the query result or access pattern of 1 : n-joins reveals the frequency of each distinct element in the column. This frequency information is used in many easy, but very detrimental inference attacks. In this paper we present a different approach: Instead of implementing a stand-alone join operator that reveals the frequency of each element in the column, we show how to construct joins over encrypted data after selection operations have been applied. These joins only leak the fine granular access pattern and frequency of elements selected for the join. Our new fine-granularly secure joins use searchable encryption and key-policy attribute-based encryption and support dynamically adding and removing database rows. Their performance is practical and we present an implementation in MySQL.
A general accuracy measure for quality of elliptic sections fitting

Least squares (LS) fitting, the most widespreadly used approach for ellipses, operates by minimizing the sum of squares of some error term measured at each data point. It is not an easy task to measure the accuracy of any fitting method in fitting elliptical sections since they rely on different error criteria. There is no unique criterion valid for any fitting method for quantifying for ellipse fitting. For this reason, there is a need for a general measure that can be used to compare the accuracy of fitted ellipses using different methods. In this work, an error measure is proposed which can be used both to measure the accuracy of any ellipse fitting method and to compare the accuracy of the ellipses fitted with different (i.e. algebraic or geometric) methods. This measure is generated from the widely known orthogonal least squares fitting (OLSF) method revising the computation scheme of initial values for the orthogonal contacting points for an ellipse in the study. This is a flexible error measure since it always computes orthogonal distance residuals between data points and the optimal ellipse and can then be used to compare the performance of different ellipse fitting methods. By computing this measure it is possible to obtain the precision of the ellipse parameters with respect to the orthogonal distance residuals. This measure is applied to the measurement of the outer section of a piston and results indicate the effectiveness of the criterion.
Automatic Detection of Fungi in Microscopic Leucorrhea Images Based on Convolutional Neural Network and Morphological Method

Leucorrhea routine test is one of the most widely used tests in gynecological examinations, and fungi inspection is vital for gynecological test because fungi is an important evidence for fungal vaginitis. In order to improve detection accuracy, an automatic identification of fungi in microscopic leucorrhea images based on convolutional neural network (CNN) and morphological method is proposed in this paper. First, we use the maximum inter-class variance method to segment original image and obtain possible fungi subimages. Then, a fully trained CNN is applied to recognize fungi. Finally, morphological method, such as template match method and concave point detection method, is used to further classify the selected candidate to improve recognize accuracy. In experiments, the method using CNN and morphological method achieved 93.26% accuracy.
Bidding Price-Based Transaction: Trust Establishment for Vehicular Fog Computing Service in Rural Area

In the time of passing through rural area, vehicles sometimes need to deal with blank spots of wireless network (4G/5G/WiFi/others). As a result, vehicles are unable to access fog computing service based on road side unit (RSU) infrastructure. To deal with this situation, vehicles utilize other vehicles' huge computational resource during their travel. However, it is not easy to put trust on other unknown vehicles since it can lead to a serious consequence for both user and server of vehicular fog computing service. In this paper, we attempt to fill gaps on establishing a trusted vehicular fog computing environment by mean of bidding price-based transaction (BPT) method. Through this method, vehicles do not need to encounter any trusted third party to have fog computing transaction with other vehicles. Moreover, every malicious activity may cause actors to lose bidding and trust, and then victim will gain redeem point as the compensation for receiving loss in transaction. In the end, this novel method is effective in limiting the number of potential attacks by mean of bidding price and payoff.
Reproductive rights or duties? The rhetoric of division in social media debates on abortion law in Poland

This study explores the argumentative schemas used in claim-making and the rhetorical resources for stance-taking in the online abortion law debate in Poland in late 2016. It shows how these discursive devices were used to divide and discredit the opponent in the social media by two social movements: the Stop Abortion coalition of conservative and religious organizations that sponsored the legislative proposal to considerably restrict abortion, and the Save Women committee that stood behind the ‘black’ protests opposing the project. The textual material is drawn from social media profiles of the two movements following a week of intense street protests and publicity activities (19–26 October 2016). It is subjected to contrastive argument analysis and critical discourse analysis of rhetorical resources. The analysis involves comparing (1) the discrepant premises underpinning arguments in the process of claim-making; (2) the reverse distribution of legitimization techniques deployed; and (3) the choices of name-calling devices aimed at discrediting antagonists.
Learning to Complete Knowledge Graphs with Deep Sequential Models

Knowledge graph (KG) completion aims at filling the missing facts in a KG, where a fact is typically represented as a triple in the form of ( head, relation, tail). Traditional KG completion methods compel two-thirds of a triple provided (e.g., head and relation) to predict the remaining one. In this paper, we propose a new method that extends multi-layer recurrent neural networks (RNNs) to model triples in a KG as sequences. It obtains state-of-the-art performance on the common entity prediction task, i.e., giving head (or tail) and relation to predict the tail (or the head), using two benchmark data sets. Furthermore, the deep sequential characteristic of our method enables it to predict the relations given head (or tail) only, and even predict the whole triples. Our experiments on these two new KG completion tasks demonstrate that our method achieves superior performance compared with several alternative methods.
Why do Designers in the "Wild" Wait to Seek Feedback until Later in their Design Process?

Receiving feedback on open-ended creative work is quintessential for success. Receiving early-stage feedback often leads to higher quality results by increasing iteration. However, when designers in the "wild" seek feedback, the majority do so towards a later stage in their design process. Moreover, online communities geared towards sharing in-progress work often fail. This paper explores why designers wait to ask for feedback until later in their design process. We guided 21 designers through two peer feedback exchanges (early- and late-stage) and used pre- and post-surveys to capture expectations and reflections, respectively. Participants viewed both stages being similarly valuable, but opt for late-stage feedback because the design space is larger and less defined, which makes the "script" for early-stage feedback less clear in practice, leading participants to avoid it. Furthermore, participants had misunderstandings regarding idea selection and the prototype fidelity necessary to elicit effective feedback. We conclude with design implications for feedback system builders.
Associative classification based on the Transferable Belief Model

Associative classification is, in essence, a frequent itemset mining technique designed specifically for classification tasks. It can be viewed as a second-order data mining problem, where the aim is to obtain an accurate classifier taking the mined set formed by class association rules (CARs) as raw material; that is, association rules in which only the class attribute is considered in the rule's consequent. Traditionally, the classifier is obtained from the initial CAR−set applying heuristic methods for sorting and pruning, leading to a (relatively small) set of classification rules. In this paper, instead of using heuristic-based methods, we propose the use of the Transferable Belief Model for obtaining an understandable, accurate and compact classifier composed of (pignistic) probability functions that summarize the huge mined set of rules. The experimental results obtained with benchmark datasets show the effectiveness of our promising proposal.
Towards Incremental Learning of Word Embeddings Using Context Informativeness

In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way.We focus on the notion of informativeness, that is, the idea that some content is more valuable to the learning process than other.We further highlight the challenges of online learning and argue that previous systems fall short of implementing incrementality.Concretely, we incorporate informativeness in a previously proposed model of nonce learning, using it for context selection and learning rate modulation.We test our system on the task of learning new words from definitions, as well as on the task of learning new words from potentially uninformative contexts.We demonstrate that informativeness is crucial to obtaining state-of-theart performance in a truly incremental setup.
Multi-task Cascaded Convolutional Neural Networks for Real-Time Dynamic Face Recognition Method

Due to the variety of poses, lighting, and scenes, dynamic face detection and calibration pose a big challenge under unconstrained environment. In this paper, we use the inherent correlation between detection and calibration to enhance their performance in a deep multi-task cascaded convolutional neural network (MTCNN). In addition, we utilize Google's FaceNet framework to learn a mapping from face images to a compact Euclidean space, where distances directly correspond to a measure of face similarity to extract the performance of facial feature algorithms. In the practical application scenario, we set up a multi-camera real-time monitoring system to perform face matching and recognition of collected continuous frames from different angles in real time.
Particle swarm optimization versus gradient based methods in optimizing neural network

Abstract Neural network modelling has become a great interest for many statisticians to be utilized in various types of data as classification, regression, and time series. It also has been applied in many fields as environment, financial, medical, agriculture and climate change. A lot of parametric methods have been developed to predict time series data such as ARIMA and exponential smoothing. However, requirement of residual assumptions causes limitedness of the models. Time series prediction by using neural network been an interesting study in the forecasting problem. In this model, one of the most interesting discussion is about how to get the optimal weights. Various gradient and non-gradient based methods have been applied in obtaining the network weights. Particle swarm optimization is one non-gradient based algorithm inspired by the behaviour of birds and fish flocks, which move to form certain formations without colliding to get the best position in a multi-dimensional space. In neural network modelling, the number of input and hidden unit give influence to the network architecture. The more complex an architecture, the more network weights must be estimated. In this study, a comparison of particle swarm optimization and some gradient based methods on the optimizing neural network was studied. Comparative studies were performed on both stationary and non-stationary data. Experiments were conducted several times to obtain optimal accuracy and stability of results, through statistics of mean and variance of MSE values.
Enhancing Emotional Effectiveness of Virtual-Reality Experiences with Voice Control Interfaces

Virtual Reality (VR) systems are becoming widespread, and can create deeply immersive experiences. Although the visual component of VR systems mimics the user’s presence in virtual spaces, dialogue control systems used in VR experiences are often adapted from older media. When selecting dialogue, older systems can break the illusion of presence in the virtual space, which is particularly jarring in interactive narratives. Using a voice control dialogue system may preserve this illusion and therefore enhance the emotional impact of narrative experiences. We evaluate this hypothesis on participants playing the same game that were assigned to use either a voice recognition or a more traditional point-and-click style interface. We find that the use of voice control with VR enhances the game’s emotional impact and users’ perceived enjoyment, as measured through a post-experiment survey instrument. We also present interesting qualitative results from interviews with participants. Our findings suggest that incorporating voice control into interactive VR narratives could improve their appeal for both consumer markets and psychological applications.
OceanSpy: A Python package to facilitate ocean model data analysis and visualization

Simulations of ocean currents using numerical circulation models are becoming increasingly realistic.At the same time, these models generate increasingly large volumes of model output data.These trends make analysis of the model data harder for two reasons.First, researchers must use high-performance data-analysis clusters to access these large data sets.Second, they must post-process the data to extract oceanographically-useful information.Moreover, the increasing model realism encourages researchers to compare simulations to observations of the natural ocean.To achieve this task model data must be analyzed in the way observational oceanographers analyze field measurements; and, ideally, by the observational oceanographers themselves.The OceanSpy package addresses these needs. SummaryOceanSpy is an open-source and user-friendly Python package that enables scientists and interested amateurs to analyze and visualize oceanographic data sets.OceanSpy builds on software packages developed by the Pangeo community, in particular Xarray (Hoyer & Hamman, 2017), Dask (Dask Development Team, 2016), and Xgcm ("Xgcm," n.d.).The integration of Dask facilitates scalability, which is important for the petabyte-scale simulations that are becoming available.OceanSpy can be used as a standalone package for analysis of local circulation model output, or it can be run on a remote data-analysis cluster, such as the Johns Hopkins University SciServer system (Medvedev, Lemson, & Rippin, 2016), which hosts several simulations and is publicly available.OceanSpy enables extraction, processing, and visualization of model data to (i) compare with oceanographic observations, and (ii) portray the kinematic and dynamic space-time properties of the circulation.
Tuning the Behavior of Context-Aware Applications

Context-aware applications are to adapt their “behavior” to the surrounding context. In this paper, we analyze different ways to achieve adequate application behavior adjustment (based on context data) and we stress upon: (i) Bayesian modeling that is not only considered useful in this regard but is also not enough explored as it concerns context-aware applications; (ii) semiotic norms that have specific relevant strengths. Even though there is much experience as it concerns the challenge of capturing context data, more knowledge is still needed about how to use context data in order to effectively make the right judgement about the “current” user situation (context state). We consider this paper’s contribution as relevant to the above-mentioned challenge.