Reviewerly/exHarmony huggingface dataset:
Visualizing Natural Environments from Data in Virtual Reality: Combining Realism and Uncertainty

Understanding complex scientific data visualizations in 2D can be challenging. Virtual Reality (VR) provides an alternative, combining realistic 3D representations with intuitive, natural interactions with data through embodied experiences. However, realistic 3D representations and associated immersive experiences are prone to misrepresentations as they are selectively representative and often leave little room for abstraction. This is particularly challenging for topics such as modeling natural environments where users value realism. We discuss the causes and categories of potential misrepresentations in VR with a particular focus on scientific visualization. We contextualize our discussion by presenting an application prototype that translates ecological model output data into a high-fidelity VR experience that allows users to walk through forests of the future. We also designed and implemented two methods to display uncertainties in high-fidelity VR environments: A multi-scenarios approach to provide users access to alternative scenarios, and a slide-and-show approach to view the environment within the confidence interval.
An Empirical Validation of Oracle Improvement

We propose a human-in-the-loop approach for oracle improvement and analyse whether the proposed oracle improvement process is helping developers to create better oracles. For this, we conducted two human studies with 68 participants overall: an oracle assessment study and an oracle improvement study. Our results show that developers exhibit poor performance (29 percent accuracy) when manually assessing whether an assertion oracle contains a false positive, a false negative or none of the two. This shows that automated detection of these oracle deficiencies is beneficial for the users. Our tool OASIs (Oracle ASsessment and Improvement) helps developers produce assertions with higher quality. Participants who used OASIs in the improvement study were able to achieve 33 percent of full and 67 percent of partial correctness as opposed to participants without the tool who achieved only 21 percent of full and 43 percent of partial correctness.
Dilation Functions in Global Optimization

Complex tasks in Computer Science can be reformulated as optimization problems, in which the global optimum of a given function must be identified. Such problems are typically noisy, multi-modal, non-convex and non-separable, and they require the application of population-based global search metaheuristics to effectively explore the search space. In this work, we address the issue of manipulating the search space of these complex optimization problems to the aim of improving the exploration and exploitation capabilities of metaheuristics. In particular, we show that the implicit assumption in global optimization problems, i.e., that candidate solutions are represented by vectors of values whose meaning has a straightforward interpretation, is not always adequate and that the semantics of parameters can be modified by re-mapping their values in the search space by means of user-defined Dilation Functions. Dilation Functions are general purpose transformations that can be applied to any metaheuristics and optimization problem to "compress" or "dilate" some regions of the search space, allowing to improve the quality of the initial population and the exploitation of promising areas, especially in the case of Swarm Intelligence algorithms. The advantages given by the application of Dilation Functions have been observed by running experiments with Fuzzy Self-Tuning Particle Swarm Optimization and Covariance Matrix Adaptation Evolution Strategies, for the optimization of the Ackley benchmark function and for the parameter estimation of a "synthetic" model of a biochemical system.
An access control scheme with fine-grained time constrained attributes based on smart contract and trapdoor

Attribute-based encryption describes the access policy with the attribute information of users. In practice, attributes usually have a certain lifespan. The existing time-based access control methods directly relate attribute keys to time. Thus, under the constraint of fine-grained time, when the attribute expires, the update of key and policy adds a large additional burden to the data user and owner. In this paper, we propose a dynamic attribute-based access control scheme to set a fine-grained valid time period for each attribute, which not only facilitates dynamic data sharing, but also enables flexible attribute revocation. We use smart contract to set valid time period for attributes. It provides smart management on users' attributes and avoids the waiting time of CSP caused by manual operations. We also introduce trapdoor that are indirectly related to time and proxy decryption method to reduce computational cost on data owners and users. Extensive security and performance analysis shows the security strength and effectiveness of the proposed scheme.
A Vision Module for Visually Impaired People by Using Raspberry PI Platform

The paper describes a vision based platform for real-life indoor and outdoor object detection in order to guide visually impaired people. The application is developed using Python and functions from OpenCV library and, ultimately ported upon Raspberry PI3 Model B+ platform. Template Matching is selected as method. More precisely, a multi-scale version approach is proposed to reduce the processing time and also to extend the detection distance range for accurate traffic sign recognition in indoor/outdoor environment. The experimental part addressed the finding of the optimum values for template and image source dimension, as well as the scaling factor.
Spatiotemporal Saliency Detection Based on Maximum Consistency Superpixels Merging for Video Analysis

Motion objects detection becomes more and more important in the applications of video surveillance, e.g., intrusion detection. The spatiotemporal saliency is an effective feature to describe object motion. However, there is a lot of redundancy in the spatial information preventing to obtain accurate saliency in an effective way. At the same time, temporal information cannot be accurately described because it is affected by uneven brightness, complex background, and fast-moving objects, especially at the edge of moving objects. In this article, we develop a novel method to tackle these problems and obtain more accurate spatiotemporal saliency. The key idea is the superpixel merging based on our maximum consistency model in feature space, through which the redundant spatial information is decreased and inhibit some temporal information errors. Experimental evaluations on the NNT dataset and surveillance videos show that the proposed method achieves better performance by comparing with some state-of-the-art methods, and can effectively detect intrusion entities.
Teachers’ Opinions on the Use of Personal Learning Environments for Intercultural Competence

Background: The aim of the present study was to evaluate teachers’ opinions on the use of Personal Learning Environments (PLE) for the development of students’ intercultural competences. Methods: This investigation carried out a thematic analysis of semi-structured interviews applied to a sample of n = 100 Compulsory Secondary Education teachers in Andalusia (Spain) with an average teaching experience of 13.13 years (SD = 6.63). The interview shows excellent content validity (Content Validity Index for the interview overall, S-CVI = 0.94). The concordant codes and sub-codes were established in the analysis with the participation of two researchers and an external expert. Then, the data was analyzed using NVivo software. Results: The results show the opinions of the teachers grouped into six separate codes: intercultural learning communities, learning improvement, intercultural development of the student, disinformation, inapplicability, and inappropriate use. Conclusions: In general, it is concluded that the teachers consider the benefits of using PLE in the development of students’ intercultural competences. Despite this, some opinions reveal a lack of training and motivation, or the lack of ability teachers have on the subject, as well as in their classrooms, with the result that the PLE is not applicable for intercultural education.
Multi-trace and multi-attribute analysis for first-break picking with the support vector machine

Many algorithms have been proposed to automate the firstbreak picking, which, however, remains a challenging problem and still requires significant human efforts. We propose an SVM (support vector machine) method with multi-trace and multi-attribute analysis to improve the automatic picking. In this method, we still use some existing picking methods (e.g., short- and long-window ratios method) to obtain advanced attributes for training. Considering the substantial correlation in waveforms between adjacent traces, we also introduce three multi-trace attributes to incorporate spatial correlation constraints into our machine learning method. Due to the large similarity of data in the same project, the machine learning model needs to be trained on only a small amount of data but can be applied to infer most of the remaining data. We adopt the SVM technique in the training process to obtain a globally optimized model and introduce multi-trace constraints to refine picking results. We test our trained model on pseudosynthetic data generated by adding random Gaussian noise into high quality real data. The root-mean-square (RMS) picking error relative to ground truth ranges from 2.4 ms to 14 ms. We also apply our trained model to real data of dynamite sources and vibroseis sources, where the results demonstrate the effectiveness of the proposed method. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Time: 11:25 AM Location: Poster Station 2 Presentation Type: Poster
Learning to Predict the Wind for Safe Aerial Vehicle Planning

Obtaining an accurate estimate of the local wind remains a significant challenge for small unmanned aerial vehicles (UAVs). Small UAVs often operate at low altitudes near terrain, where the wind environment can be more complex than at higher altitudes. Combined with their relatively low mass, this makes small UAVs particularly susceptible to wind. In this paper we present an approach for predicting high-resolution wind fields based on a terrain elevation model and known inflow conditions. Our approach uses a deep convolutional neural network (CNN) to generate 3D wind estimates. We show that our approach produces wind estimates with lower prediction error than existing methods, and that inference can be performed on an on-board computer in less than two seconds. By providing the wind estimate to a sampling-based planner we show that the improved estimates allow the planner to generate safer paths in strong wind scenarios than with alternative wind estimation techniques.
Deep Learning for Sentiment Analysis of Arabic Text

Deep learning has been very successful in the past decades, especially in Computer Vision and Speech Recognition fields. It has been also used successfully in the Natural Language Processing field because of the availability of an enormous amount of online text data, such as social networks and reviews websites, which have gained a lot of popularity and success in the past years. Sentiment Analysis is one of the hottest applications of Natural Language Processing (NLP). Many researchers have done excellent work on Sentiment Analysis for English language. However, the amount of work on Sentiment Analysis for Arabic language is, in comparison, very limited due to the complexity of the Arabic language's morphology and orthography. Unlike the English language, Arabic has many different dialects which makes Sentiment Analysis for Arabic more difficult and challenging, especially when working on data collected from social networks, which is known to be unstructured and noisy. Most of the work that has been done on Sentiment Analysis of Arabic language, focused on using lexicons and basic machine learning models. In addition, most of the work has been done on small datasets because of the limited number of the available annotated datasets for Arabic language. This paper proposes state-of-the-art research for Sentiment Analysis of Arabic microblogging using new techniques, and a sophisticated Arabic text data preprocessing.
A probabilistic framework to incorporate mixed-data type features: Matrix factorization with multimodal side information

Recommender systems that exclusively rely on past interactions between the users and the items underperform in settings with very few observations. Their performance at such extreme sparsity can be improved by exploiting the side information of the users and the items including the demographics and the item descriptions. However, such side information is mostly heterogeneous and multimodal, including both numerical and categorical features to yield a non-trivial incorporation process. Researchers have addressed this problem mainly by converting the categorical features into numerical ones or forming numerical similarity matrices. This paper presents a different approach in the form of a novel Bayesian probabilistic generative framework which can effectively incorporate multimodal side information into the matrix factorization (MF-MSI) model. With the help of local quadratic bounds on the categorical likelihoods, we derive a scalable and computationally efficient iterative optimization method based on the variational EM to learn the posterior distributions of latent variables for both the users and the items. A comprehensive experimental study on both simulated and real benchmark datasets demonstrate the proof-of-concept where the additional side information improves both the prediction accuracy and the ranking performance over more than a dozen popular baseline models. Finally, the proposed MF-MSI model claims state-of-the-art performance in the majority of the test scenarios when compared to more recently introduced recommender systems which can also utilize the side information via different techniques.
Emotional Speech Synthesis Based on Style Embedded Tacotron2 Framework

In this paper, we propose a speech synthesis system that effectively generates multiple types of emotional speech using the concept of global style token (GST); where the emotion-related style information is presented by an additional style embedding vector. Although the GST is not a new idea, no one has been utilized the idea for an emotional speech synthesis task. We explicitly combine the GST idea with the Tacotron2 framework to implement an emotional text-to-speech system. The analysis results demonstrate that the proposed GST structure successfully transfers various types of emotional information to the synthesized speech. Subjective listening tests to evaluate the naturalness and emotional expression of synthesized speech are conducted to verify the superiority of the proposed algorithm.
MESH: A Flexible Distributed Hypergraph Processing System

With the rapid growth of large online social networks, the ability to analyze large-scale social structure and behavior has become critically important, and this has led to the development of several scalable graph processing systems. In reality, however, social interaction takes place not only between pairs of individuals as in the graph model, but rather in the context of multi-user groups. Research has shown that such group dynamics can be better modeled through a more general hypergraph model, resulting in the need to build scalable hypergraph processing systems. In this paper, we present MESH, a flexible distributed framework for scalable hypergraph processing. MESH provides an easy-to-use and expressive application programming interface that naturally extends the "think like a vertex" model common to many popular graph processing systems. Our framework provides a flexible implementation based on an underlying graph processing system, and enables different design choices for the key implementation issues of partitioning a hypergraph representation. We implement MESH on top of the popular GraphX graph processing framework in Apache Spark. Using a variety of real datasets and experiments conducted on a local 8-node cluster as well as a 65-node Amazon AWS testbed, we demonstrate that MESH provides flexibility based on data and application characteristics, as well as scalability with cluster size. We further show that it is competitive in performance to HyperX, another hypergraph processing system based on Spark, while providing a much simpler implementation (requiring about 5X fewer lines of code), thus showing that simplicity and flexibility need not come at the cost of performance.
A highly efficient and secure hardware implementation of the advanced encryption standard

A highly efficient and power analysis attack robust architecture for hardware implementation of the advanced encryption standard algorithm (AES) is presented. By choosing a correct topology the required resources for the FPGA implementation of the AES algorithm have been reduced. In addition, by using an innovative scheme which combines a randomized SBox with a modified Boolean masking technique, the correlation between Hamming distance of sensitive data and power consumption of the algorithm on target platform is removed. The robustness of the proposed outer masking which is a modified version of the existing first-order Boolean masking scheme is evaluated by the Welch's t-test statistical analysis and also experimental results while the efficiency of the internal randomization technique inside the SBox module is based on the randomization in the underlying composite field GF(24)2. The proposed implementation outperforms other proposals presented in the open literature as the results from the Place and Route report indicate that area occupied by the unprotected architecture is 746 slices with a maximum clock frequency of 318.4 MHz on Virtex-5 FPGA while the protected implementation consumes 966 slices with a maximum frequency of 310.4 MHz.
High-Fidelity Reversible Data Hiding Using Block Extension Strategy

Improved pixel value ordering (I-PVO) is a popular reversible data hiding scheme which uses sorting-based prediction error expansion method to provide high fidelity marked images. In this paper, a new reversible data hiding scheme for large payload is presented. The proposed scheme first embeds the secret data into 2×2 sub-block using I-PVO method and then extends the size of block 1×2 pixels if the noise level of the sub-block is equal to `0'. The additional pixels of the block are modified to embed the secret data using the proposed block extension strategy. The proposed block extension strategy uses a new prediction and embedding method that can hide the secret data with minimal distortion. As a result, high embedding capacity along with high quality marked image is achieved. The experimental results show that the proposed method has superior performance over the previous PVO based methods.
On Language Levels for Feature Modeling Notations

Configuration is a key enabling technology for the engineering of systems and software as wells as physical goods. A selection of configuration options (aka. features) is often enough to automatically generate a product tailored to the needs of a customer. It is common that not all combinations of features are possible in a given domain. Feature modeling is the de-facto standard for specifying features and their valid combinations. However, a pivotal hurdle for practitioners, researchers, and teachers in applying feature modeling is that there are hundreds of tools and languages available. While there have been first attempts to define a standard feature modeling language, they still struggle with finding an appropriate level of expressiveness. If the expressiveness is too high, the language will not be adopted, as it is too much effort to support all language constructs. If the expressiveness is too low, the language will not be adopted, as many interesting domains cannot be modeled in such a language. Towards a standard feature modeling notation, we propose the use of language levels with different expressiveness each and discuss criteria to be used to define such language levels. We aim to raise the awareness on the expressiveness and eventually contribute to a standard feature modeling notation.
Knowledge acquisition from parsing natural language expressions for humanoid robot action commands

In this paper we propose an approach that allows the NAO humanoid robot to execute natural language commands spoken by the user. To provide the robot with knowledge, we have defined an action robot ontology. The ontology is fed to an NLP engine that performs a machine reading of the input text (in natural language) given by a user and tries to identify action commands for the robot to execute. The system can work in two modes: STATELESS and STATEFUL. In STATELESS mode, each human expression correctly interpreted by the robot as an action command is performed by NAO which returns in its default posture afterwards. When in STATEFUL mode, the robot has knowledge of its current posture and performs the command only if it is compatible with its current state. In this mode, the robot does not return to its default posture. For example, if the user had told the robot to stand on its right leg in a first command, the robot cannot perform a following command stating to stand on its left leg as the two actions (raise left leg and raise right leg are incompatible). For each action that the robot can perform we modeled a corresponding element in the ontology that also includes a list of associated compatible and non-compatible actions. Our system also handles compound expressions (e.g., move your arms up) and multiple expressions (different commands within one sentence) that the robot understands and performs.
NN-Based Czech Sign Language Synthesis

This paper describes our Czech sign language synthesis that converts a Czech text into a series of skeletal poses. Our main goal is to avoid demanding handcrafted annotations of videos and to avoid a manual mapping between sign language glosses and skeletal poses. Thus, instead of solving these task separately, we join a model of an implicit neural-network-based translator and a model of the mapping between sign language glosses and we train both models together. For this purpose, we propose a simple differentiable operation that decomposes input symbols and it allows to produce a required series without any recurrent mechanism. We used The OpenPose toolbox to automatically extract skeletal poses and we designed a gradient-descend-based algorithm that converts a 2D skeleton model to a 3D skeleton model in order to fix misplaced and missing joints. Weather forecast parts of The daily news in Czech sign language were used to obtain our training and testing data. Our experiments demonstrate the benefit of the implicit translator and an ability of the designed sign language synthesis system to produce naturally formed skeletal poses.