Reviewerly/exHarmony huggingface dataset:
Sustainable Production and Consumption in 360°

Utilizing virtual reality to foster empathy is an increasingly explored research topic. While this research is often focused on a viewer's feelings towards and understanding of people depicted in the virtual experience, perhaps virtual reality and immersive environments such as 360° video could impact a viewer's impression of not a person but of complex environmental issues like sustainability.SCP in 360°: Sustainable Production and Consumption in 360 Degrees is a series of six 360° videos aiming to make sustainable production and consumption engaging, memorable, and relatable to a wider audience. Produced by the United Nations' One Planet network and researchers at Simon Fraser University, SCP in 360° takes viewers around the world to see the work of the One Planet network on the ground, including projects on sustainable building in Nepal, community-based tourism in South Africa, consumer information in Chile, sustainable and healthy gastronomy in Costa Rica, circular procurement in the Netherlands, and low-carbon sustainable lifestyle initiatives in rural Armenia.Researchers at Simon Fraser University conducted a study with visitors who watched these videos as part of an exhibit at the United Nations Headquarters in New York City to investigate if viewers better understand concepts and values around sustainability or if they care more about sustainability after viewing such videos in a 360° environment. This research explores if and how new immersive visual technologies might better communicate and transmit values and the importance of sustainability efforts.
Tuser2: A New Method for Twitter and Youtube Matching Profiles

Matching user profiles is an efficient way to map users across social networks and communicate an accurate portrait of a user. This study can be applied in different application domains such as recommendation, privacy, cyber-security, etc. In this paper, we address the problem of matching profiles in two popular social networks: YouTube and Twitter. First, we identify users by their shared publicly information and private information implicitly extracted from their published contents. Based on this information, we propose a method that matches profiles in both social networks using different features extraction. Then, we propose an algorithm we call Tuser2 that returns for a targeted profile in Twitter its matched profiles in YouTube and vice versa based on public and inferred attributes comparison. Our preliminary results show that profiles in YouTube and Twitter can be matched with high accuracy. Therefore, exploiting both publicly shared attributes and inferred ones can improve the reliability of the results.
Sound Space: Communicating Acoustics Through Interactive Visualization

We present Sound Space, an interactive and engaging environment in which to explore some of the more basic concepts and consequences of acoustic design. By presenting acoustic data visually rather than aurally, we give the user the opportunity to pause, rewind, and fast forward the sound propagation in the space. As an immersive three dimensional visualization, the designer can gain a better spatial understanding of the relationship between a room's geometry and its acoustic performance. With a focus on both accuracy and design aesthetics, we present acoustic data through a compelling and fun experience in order to increase the user's engagement and understanding of the data. The resulting visualization is immersive and dynamic, allowing a designer to interact with the space, make changes, and see the consequences in real time. This reduces barriers to acoustic considerations in early design phases, and can aid the sound design of space that would not traditionally have an acoustic focus, but which can still benefit significantly from improved acoustic considerations.
Noise identification and removal by using higher order statistics and ICA Transformation for satellite images

Satellite images play a crucial role in meteorological operations for analyzing the atmospheric related information, urban & vegetation studies and sea surface temperatures. This information is adversely affected by noise pixels that are additionally added into an image, the spatial and spectral characteristics and pixel information are also changed by these components. The identification of noises is very important, if the noise is identified, suitable filters are applied to remove the noise from the actual image. The identification of impulse and speckle noises are very difficult if it exceeds above 40%.Using this proposed technique, the speckle and impulse noises are identified by the Higher order statistics which is based on smoothing factor and later it removes noises using Independent component analysis transform method. The proposed method gives the best PSNR when compared with other standard methods.
A METHOD FOR CLASSIFYING USABILITY FINDINGS TO ENHANCE VALIDATION OF NEW HEURISTICS

Different usability heuristics have been proposed as new application domains arise. Such proposals usually depend on the validation of the new heuristics. However, current validation methods are still biased by subjective comparisons of usability findings. In this paper, we aimed to enhance the process of matching usability finding descriptions and mitigate the bias of such process. To reach our goal, we adopted ontology techniques to extend the User Action Framework for the context of validating new usability heuristics. We tested three hypotheses about the feasibility of our new framework based on a case study with 173 usability findings. These usability findings were retrieved from an online project of a private mobile browser. Our data analysis of supported merging three classification schemes for our framework: User Action Framework, Typical Usability Defects (from ISO) and the heuristics of Nielsen. Finally, we describe a logical process for our method, because some of the contents from the classification schemes are not disjoint.
Continuous Adaptation Management in Collective Intelligence Systems

Collective Intelligence Systems (CIS), such as wikis and social networks, enable enhanced knowledge creation and sharing at organization and society levels. From our experience in R&D projects with industry partners and in-house CIS development, we learned that these platforms go through a complex evolution process. A particularly challenging aspect in this respect represents uncertainties that can appear at any time in the life-cycle of such systems. A prominent way to deal with uncertainties is adaptation, i.e., the ability to adjust or reconfigure the system in order to mitigate the impact of the uncertainties. However, there is currently a lack of consolidated design knowledge of CIS-specific adaptation and methods for managing it. To support software architects, we contribute an architecture viewpoint for continuous adaptation management in CIS, aligned with ISO/IEC/IEEE 42010. We evaluated the viewpoint in a case study with a group of eight experienced engineers. The results show that the viewpoint is well-structured, useful and applicable, and that its model kinds cover well the scope to handle different CIS-specific adaptation problems.
Point-Placement Techniques and Temporal Self-Similarity Maps for Visual Analysis of Surveillance Videos

The manual analysis of surveillance videos is unfeasible due to the excessive amount of data, the associated subjectivity, or the eventual presence of distracting noise. Automatic summarization approaches provide little/no user interaction, limiting his/her comprehension regarding the involved phenomena. Visual analytics techniques represent a potential tool for such analysis, providing video representations that clearly communicate their content, potentially revealing patterns that may represent events of interest. In this paper, we present a methodology for visual analysis of surveillance video that combines point-placement techniques and Temporal Self-similarity Maps (TSSMs) to reveal the events occurrence structure and to enhance the comprehension of their temporal properties. Experiments in several surveillance scenarios demonstrate that our proposed methodology provides an effective events summarization and the exploration of both the structure of each event and the relationship among them, allowing the security agent to filter/explore those that represent potential alert situations.
Fractal analysis for classification of breast lesions

Nowadays, breast lesions are a common health problem among women. Breast thermograms are images recorded by digital-optical systems with high resolution that use infrared technology in order to show vascular and temperature changes. In the present work, we study benign and malignant breast lesions shape by means of fractal analysis. The Fractal Dimension (FD) is calculated with the Box Counting method and the Hurst exponent is obtained using the Wavelet coefficients and the Detrending Moving Average algorithm. These algorithms was applied to synthetic images and breast thermograms. The Fractal Dimension value is used for patient classification with or without breast injury. The proposed methodology was applied to the Database For Mastology Research (DMR) in order to classify thermographic images. The FD of ROIs for breast thermograms was calculated. Results shows that the FD BCM values ranges from [0.45,0.81] in 4 healthy cases and from [0.92,1.33] in 4 unhealthy cases.
A Robotic Chinese Stroke Generation Model Based on Competitive Swarm Optimizer

The process of neural network based robotic calligraphy involves a trajectory generation process and a robotic manipulator writing process. The writing process of robotic writing cannot be expressed by mathematical expression; therefore, the conventional gradient back-propagation method cannot be directly used to optimize trajectory generation system. This paper alternatively explores the possibility of using competitive swarm optimizer (CSO) algorithm to optimize the neural network used in the robotic calligraphy system. In this paper, a variational auto-encoder network (VAE) including an encoder and a decoder is used to establish the trajectory generation model. The training of the VAE is divided into two steps. In Step 1, the decoder part of VAE network is trained by using the gradient descent method to extract the features of the input strokes. In the second step, the first encoder is used to obtain the image features directly as the input of the decoder, and the writing sequence of stroke trajectory points is obtained directly by the decoder. CSO is applied to train the decoder of VAE. Then the writing sequence is sent to the robot manipulator for writing. Experiments show that the strokes generated by this method can achieve similar but slightly different strokes from the training samples, so that the stroke writing diversity can be retained by VAE. The results also indicate the potential in autonomous action-state space exploration for other real-world applications.
Active Image Data Augmentation

Deep neural networks models have achieved state-of-the-art results in a great number of different tasks in different domains (e.g., natural language processing and computer vision). However, the notions of robustness, causality, and fairness are not measured in traditional evaluated settings. In this work, we proposed an active data augmentation method to improve the model robustness to new data. We use the Vanilla Backpropagation to visualize what the trained model consider important in the input information. Based on that information, we augment the training dataset with new data to refine the model training. The objective is to make the model robust and effective for important input information. We evaluated our approach in a Spinal Cord Gray Matter Segmentation task and verified improvement in robustness while keeping the model competitive in the traditional metrics. Besides, we achieve the state-of-the-art results on that task using a U-Net based model.
Feature Importance Identification through Bottleneck Reconstruction

We address the problem of feature importance. Often, when working with classification or regression problems, the results of black-box deep learning techniques are held to scrutiny in an effort to interpret which and to what extent various features affect outcome. We address this issue specifically when the model has a bottleneck which we will be used to infer feature importance. In this paper, we apply this technique to weather data and study which weather features affect traffic most. To this end, we introduce convolutional spatial embedding to convert data with spatial information into spatial images that are suitable for convolutional neural networks. An advantage of our approach is in dealing with input that has highly correlated features, where removing even an important feature will not increase loss.
Improving Ancient Cham Glyph Recognition from Cham Inscription Images Using Data Augmentation and Transfer Learning

Ancient Cham glyphs have mostly appeared in inscriptions on stones at some museums in Vietnam. Unfortunately, these inscriptions are being abrasive by the time. To conserve Cham heritage as well as to make them widely accessible and readable by users, digitization and recognition of ancient Cham glyphs become necessary. In our previous work, we have built the first dataset of champ inscription images, manually segmented them in glyphs and annotated by an ancient Cham expert. We adapted some automatic recognition methods and conducted experiments on the manually denoised dataset. The aim of this paper is to extend that earlier research to work on noising data. To this end, we face two main issues. Firstly, the current pre-built dataset is still small which is usually a main drawback for deep learning based methods. Therefore, some data augmentation techniques will be evaluated and investigated to increase the number and variation of samples in the dataset. Second, even with the augmented dataset, the fact of training a deep model from scratch could be very long and sometimes cannot meet a good local minimum. Therefore, we use a simple transfer learning procedure which inherits knowledge from similar or of the same family language. Experiments on both the raw test set and its denoised version show very promising results ( $$64.4\%$$ and $$88.5\%$$ of F1-score on two test sets respectively).
Properties of Bent Functions in the Truth Domain

In this paper we study bent functions in the truth vector domain. While in general bent functions are defined on the Walsh basis [-1,1] we look on their properties and classification in the standard Boolean basis [0,1]. We show that applying the Welsh transform to Boolean functions of n variables in the truth domain, results in a classification that allows to rank bent functions. We investigate the classification of bent vs non bent functions using standard machine learning and compare the performance difference. Finally we extend our search for efficient classification criteria to functions with smaller amount of variables and show the amount of classification possible.
You Have the Choice: The Borda Voting Rule for Clustering Recommendations

Automatic recommendations are very popular in E-commerce, online shopping platforms, video on-demand services, or music-streaming. However, recommender systems often suggest too many related items such that users are unable to cope with the huge amount of recommendations. In order to avoid losing the overview in recommendations, clustering algorithms like k-means are a very common approach to manage large and confusing sets of items. In this paper, we present a clustering technique, which exploits the Borda social choice voting rule for clustering recommendations in order to produce comprehensible results for a user. Our comprehensive benchmark evaluation and experiments regarding quality indicators show that our approach is competitive to k-means and confirms the high quality of our Borda clustering approach.
Optical image and microchemical analysis of gold grains from a weathered profile of the Minvoul greenstone belt, northern Gabon

Abstract Morphological characterization and quantification of gold particles by optical image analysis (OIA) and by compositional analysis of microprobes using scanning electron microscope and electron microprobe analysis techniques were carried out on gold grains from the Minvoul area (Archean greenstone belt in Gabon). Large grains of almost pure gold were found throughout a weathering profile, which consisted of saprolite, mottled clay zone, iron duricrust, pisolitic gravels and yellow latosol. In the deeper horizons, gold was dissolved as shown by corrosion features on the surface of particles with average sizes of 2.6Φ and 2.35Φ in the saprolite and mottled clay zones, respectively. The occurrence of secondary gold in the duricrust was indicated by the larger size of the nuggets (average, 1.8Φ), the high fineness (&gt; 995 in average) and the close textural relationship between gold and neoformed iron oxyhydroxides. The uppermost horizons composed of yellow latosol and pisolitic gravels were interpreted as transported materials based on their size distribution (average 0.85Φ and 1.34Φ), sorting and shape parameters. The best morphological parameter to describe the whole weathering profile was found to be the perimeter/area ratio. The highest ratios were recorded in the saprolite (average 0.192 μm −1 ), and decreased towards the surface (average 0.057 μm −1 ). The combination of the OIA technique and the microchemical analysis of gold grains allowed us to define specific morphological and compositional characteristics of the gold particles for each horizon. Both methods proved to be of great utility to understand gold concentration, dissolution and dispersion processes in supergene environments.
User-guided Dimensionality Reduction Ensembles

Dimensionality Reduction (DR) techniques are widely used to analyze and make sense of high-dimensional data. Each method is geared towards preserving a different aspect of the data. For example, some techniques favor neighborhood preservation whereas others favor distance preservation. While these DR techniques help users to represent their data, it makes a complex task to select a suitable DR. Also, most DR techniques have additional parameters that affect the results, which make the task of choosing a technique more difficult. Existing methods compare DR techniques using some quality metrics, and some of them combine DR outputs by averaging projections. However, it does not yet provide enough mechanisms to create a new DR according to user requirements. In this paper, we present a way to analyze and compare different DR techniques. It is an interactive assessment method that allows a user to explore known DR techniques, identify the differences between them, and create a new DR technique that combines existing techniques to match user expectations.
An Efficient Signature Scheme From Supersingular Elliptic Curve Isogenies

Since supersingular elliptic curve isogenies are one of the several candidate sources of hardness for building post-quantum cryptographic primitives, the research of efficient signature schemes based on them is still a hot topic.In this paper, we present a many-time signature scheme based on the hash function from supersingular elliptic curve isogenies over the finite field F p 2 where p = 2 521 -1.Our signature scheme achieves smaller signature sizes relative to other post-quantum signature schemes based on supersingular elliptic curve isogenies, such as Galbraith's signature schemes (AsiaCrypt 2017) and Yoo's scheme (FC 2017).The structure of our scheme follows that of the hash-based signature scheme submitted to National Institute of Standards and Technology for post-quantum cryptography in 2018 with some modifications.To complete the construction, we firstly apply the method of Weil restriction to improve the efficiency of hash function from supersingular elliptic curve isogenies by approximately 30%, then propose a new Winternitz one-time signature scheme based on the hash function.Finally, we implement the signature scheme.
A Road to Ultrafilter Extensions

We propose a uniform method of constructing ultrafilter extensions from canonical models, which is based on the similarity between ultrafilters and maximal consistent sets. This method can help us understand why the known ultrafilter extensions of models for normal modal logics and for classical modal logics are so defined. We then apply this method to obtain ultrafilter extensions of models for Kripke contingency logics and for neighborhood contingency logics.
Quantitative Requirement Evaluation for a European Refugee Management System serving conflicting Needs of Migrants, GOs, and NGOs in correct and fair operative Form

The so-called "refugee crisis" in 2015 overstrained the European authorities and people seeking for help. Many refugee camps in Europe were not adequately prepared and had to process a large number of people. There was no standardized way to handle and coordinate people within a refugee camp. The EU Member States defined different approaches to identify and register refugees on their territory. The authors already proposed a standardized refugee management system concept that should enhance the identification and registration process in a European refugee camp. They introduced 14 functional requirement areas and evaluated them through qualitative research with experts and national authorities. Due to ethical and organizational issues, the authors decided to apply a quantitative survey about the acceptance of the proposed system requirements, which are presented in this paper. Several national and European institutions, companies, and scientists who are involved in the refugee administration should demonstrate the practicability of the proposed system. The results of the survey affirmed the general acceptance of all requirement areas. Furthermore, the authors analyzed the given answers in more detail to understand why specific branches accepted or declined the requirement areas.
Avian Based Intelligent Algorithm to Provide Zero Tolerance Load Balancer for Cloud Based Computing Platforms

Artificial intelligence changes the art of solving the computational problems from defined computing structures into disorganized computing structures with the interference of naturally-inspired activities. On this basis, many algorithms were proposed and applied successfully, those results give complete as well as partial solutions for their applications. In this article, the authors consider the same phenomena and the investigation area is a load balancer. The legitimate aim is to bring a zero-tolerance load balancer by applying artificial intelligence techniques. For this, the authors introduced an algorithm named the Eagle Fly algorithm, which is a natural inspired algorithm, completely based on eagle characteristic behavior. From this, the authors examine how tasks are fetched, computed, and server on-demands are supported. This article proves performance metrics received from eagle fly algorithm is good and the results were compared with other existing natural inspired algorithms.