Reviewerly/exHarmony huggingface dataset:
Enabling Artistic Control Over Pattern Density and Stroke Strength

Despite the remarkable results and numerous advancements in neural style transfer, achieving artistic control is still a challenging feat, primarily since existing methodologies treat the style representation as a black-box model. This oversight significantly limits the range of possible artistic manipulations. In this paper, we propose a method to enable artistic control on any correlation-based style transfer models along with guiding intuitions. Our focus is on controlling two perceptual factors: Pattern Density and Stroke Strength. To achieve this, we introduce the centered Gram style representation and manipulate it with our variance-aware adaptive weighting and correlation-based selective masking. Through several experiments and comparisons with the state-of-the-art, we show that we can achieve artistic control with competitive stylization quality. Additionally, since our method involves manipulating style representation, it can easily be adapted to popular style transfer models. We analyze different style representation properties to propose rules that govern the style transfer process, which is critical towards achieving artistic control over pattern density and stroke strength.
Feasible and stressful trajectory generation for mobile robots

While executing nominal tests on mobile robots is required for their validation, such tests may overlook faults that arise under trajectories that accentuate certain aspects of the robot's behavior. Uncovering such stressful trajectories is challenging as the input space for these systems, as they move, is extremely large, and the relation between a planned trajectory and its potential to induce stress can be subtle. To address this challenge we propose a framework that 1) integrates kinematic and dynamic physical models of the robot into the automated trajectory generation in order to generate valid trajectories, and 2) incorporates a parameterizable scoring model to efficiently generate physically valid yet stressful trajectories for a broad range of mobile robots. We evaluate our approach on four variants of a state-of-the-art quadrotor in a racing simulator. We find that, for non-trivial length trajectories, the incorporation of the kinematic and dynamic model is crucial to generate any valid trajectory, and that the approach with the best hand-crafted scoring model and with a trained scoring model can cause on average a 55.9% and 41.3% more stress than a random selection among valid trajectories. A follow-up study shows that the approach was able to induce similar stress on a deployed commercial quadrotor, with trajectories that deviated up to 6m from the intended ones.
Incremental method for multiple line detection problem — iterative reweighted approach

In this paper we consider the multiple line detection problem by using the center-based clustering approach, and propose a new incremental method based on iterative reweighted approach. We prove the convergence theorem and construct an appropriate algorithm which we test on numerous artificial data sets. A stopping criterion in the algorithm is defined by using the parameters from the DBSCAN algorithm. We give necessary conditions for the most appropriate partition, which have been used during elimination of unacceptable center-lines that appear in the output of the algorithm. The algorithm is also illustrated on a real-world image coming from Precision Agriculture.
Operationalizing Intentionality to Play <i>Hanabi</i> With Human Players

The cooperative card game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hanabi</i> has become of increasing interest in the community, since it combines partially hidden information with information exchange using restricted communication channels. In this article, we describe artificial intelligence agents that are designed to play the game with human players. Our agents make use of the fact that human players expect other players to act intentionally by formulating goals of their own and planning how to achieve them. They then use the available actions available to communicate their plan to the human player. On the flip side, our agents also interpret the actions performed by the human player as containing information about their plans. We present two different variants of our agent that perform this interpretation in different ways. Additionally, since part of human communication happens in subtle indirect ways, we also demonstrate that our agent can use the timing of the human player’s actions as additional information. In order to validate our agents, we have performed two separate experiments: one was done to validate the intentional component of the agents, while the other focused on the interpretation of received information. In this article, we also present the results obtained from these two experiments.
Generating summary using sequence to sequence model

With the advent of the information explosion era, automatic summarization of text abstracts has become more and more popular in academic circles in natural language processing. At present, there are many problems in the process of generating text abstracts. In view of the problem of gradient disappearance, this paper uses the advantages of LSTM in dealing with long-distance dependence of sequence data to alleviate this problem, making the model pay more attention to more effective information. In order to further improve the performance of the model, attention mechanism is introduced to solve the problem of insufficient semantic understanding and unsmooth statement. The seq2seq method based on LSTM and attention mechanism designed in this paper can deal with the problems existing in generating abstracts and get better effect of generating text abstracts, which can be applied to text extraction tasks.
Co‐creating Visualizations: A First Evaluation with Social Science Researchers

Abstract Co‐creation is a design method where designers and domain experts work together to develop a product. In this paper, we present and evaluate the use of co‐creation to design a visual information system with social science researchers in order to explore and analyze their data. Co‐creation proposes involving the future users in the design process to ensure that they play a critical role in the design, and to increase the chances of long‐term adoption. We evaluated the co‐creation process through surveys, interviews and a user study. According to the participants’ feedback, they felt listened to through co‐creation, and considered the methodology helpful to develop visualizations that support their research in the near future. However, participation was far from perfect, particularly early career researchers showed limited interest in participating because they did not see the process as beneficial for their research publication goals. We summarize benefits and limitations of co‐creation, together with our recommendations, as lessons learned.
Challenges in Supporting Exploratory Search through Voice Assistants

Voice assistants have been successfully adopted for simple, routine tasks, such as asking for the weather or setting an alarm. However, as people get more familiar with voice assistants, they may increase their expectations for more complex tasks, such as exploratory search --- e.g., "What should I do when I visit Paris with kids? Oh, and ideally not too expensive." Compared to simple search tasks such as "How tall is the Eiffel Tower?", which can be answered with a single-shot answer, the response to exploratory search is more nuanced, especially through voice-based assistants. In this paper, we outline four challenges in designing voice assistants that can better support exploratory search: addressing situationally induced impairments; working with mixed-modal interactions; designing for diverse populations; and meeting users' expectations and gaining their trust. Addressing these challenges is important for developing more "intelligent" voice-based personal assistants.
Virtual and Augmented Reality Applied in Power Electric Utilities for Human Interface Improvement – A Study Case for Best Practices

With the emergence of new technologies and the need to make processes more sustainable, cities and utilities tend to become increasingly “smarter”. It is no different for electric power system and its grid services. Therefore, the digitalization of the electrical Utilities is more and more assiduous. It contributes to a better supply of energy with more reliability, as well as the reduction of operational and maintenance cost. For what concerns the grid services, the workforce is a constantly target to improvement. Doing so, the Utilities Human Interface sector can be enhanced with digital technological trends. For instance, virtual and augmented realities is used to sharpening cognitive and on-field operational aspects. These technologies have applicability that involves learning process, training improvement, faster problem solution, facilitation of operations, etc. This paper aims to evaluated VR and AR looking at its trends toward an innovative technology applied to Electrical Utilities. Likewise, indicate the best practice application that was find during a Brazilian R&D project, named Urban Futurability, developed in a Brazilian Electrical Distribution Company.
Multi-target path planning for mobile robot based on improved PSO algorithm

An improved particle swarm optimization algorithm and the objective function of path planning are presented in this paper. First, the inverse learning strategy is used to initialize the particle swarm. At the same time, the inertia weight and learning factor are dynamically adjusted according to the iteration process of the algorithm, and the global search ability of the optimization algorithm is improved. At the same time, the convergence accuracy and stability of the algorithm are improved. In this paper, the selection of target points for mobile robots is transformed into solving TSP problem, and the improved particle swarm optimization algorithm is used to solve TSP problem and the optimization of path planning objective function. The experimental results show that the improved algorithm has better optimization ability, better security and smoothness of the robot trajectory, and improves the efficiency of multi-objective point path planning of the robot.
VA‐TRAC: Geospatial Trajectory Analysis for Monitoring, Identification, and Verification in Fishing Vessel Operations

Abstract In order to ensure sustainability, fishing operations are governed by many rules and regulations that restrict the use of certain techniques and equipment, specify the species and size offish that can be harvested, and regulate commercial activities based on licensing schemes. As the world's second largest exporter offish and seafood products, Norway invests a significant amount of effort into maintaining natural ecosystem dynamics by ensuring compliance with its constantly evolving science‐based regulatory body. This paper introduces VA‐TRAC, a geovisual analytics application developed in collaboration with the Norwegian Directorate of Fisheries in order to address this complex task. Our approach uses automatic methods to identify possible catch operations based on fishing vessel trajectories, embedded in an interactive web‐based visual interface used to explore the results, compare them with licensing information, and incorporate the analysts’ domain knowledge into the decision making process. We present a data and task analysis based on a close collaboration with domain experts, and the design and implementation of VA‐TRAC to address the identified requirements.
Using Deep Learning to Find Victims in Unknown Cluttered Urban Search and Rescue Environments

We investigate the first use of deep networks for victim identification in Urban Search and Rescue (USAR). Moreover, we provide the first experimental comparison of single-stage and two-stage networks for body part detection, for cases of partial occlusions and varying illumination, on a RGB-D dataset obtained by a mobile robot navigating cluttered USAR-like environments. We considered the single-stage detectors Single Shot Multi-box Detector, You Only Look Once, and RetinaNet and the two-stage Feature Pyramid Network detector. Experimental results show that RetinaNet has the highest mean average precision (77.66%) and recall (86.98%) for detecting victims with body part occlusions in different lighting conditions. End-to-end deep networks can be used for finding victims in USAR by autonomously extracting RGB-D image features from sensory data. We show that RetinaNet using RGB-D is robust to body part occlusions and low-lighting conditions and outperforms other detectors regardless of the image input type.
Residual Forward-Subtracted U-Shaped Network for Dynamic and Static Image Restoration

Advanced image sensors with high resolution are now being developed for specially purposed electro-optical systems, with research focused on robust image quality performance in terms of super resolution and noise removal under various environmental conditions. Recently, machine-learning and deep-learning methods have been studied as the best practical techniques for restoration to improve the deteriorated image quality of sensors. However, these methods show limitations and side effects of image degradation such as image non-uniformity. In this paper, we analyze and randomly generate additive white Gaussian noise, non-uniform line noise, and dark saturation as representative image degradations. We then propose an advanced U-net model based on global and local residual learning in order to restore complexly deteriorated images. The proposed method shows unparalleled performance compared to alternative models and previous studies. In particular, various complex noise components are minimized and improved with equal quality so that variation between sequential images is minimized. These findings leverage mutual corroboration of quantitative and qualitative evaluation metrics. In the future, the proposed model is expected to contribute to a wide range of field applications such as defense, surveillance, and video media for image quality enhancement technologies.
Phase noise reduction in wavelength scanning interferometry using a phase synthesis approach

We proposed a method to reduce the phase noise in wavelength scanning interferometry (WSI). The method utilizes the two different mechanisms of Fourier transform and the least squares algorithm to produce a synthetic phase map. For validation, the deformation of a fiberglass reinforced resin matrix composite specimen under mechanical loading was measured using a self-built WSI system. The results demonstrate the decrease of phase noise points by more than 20% without sacrificing spatial resolution. The reduction of phase noise can provide high quality phase unwrapping and increase the accuracy of deformation measurements.
Risks and Benefits of Initial Coin Offerings: Evidence from impak Finance, a Regulated ICO<sup>*</sup>

ABSTRACT This study provides a better understanding of the business and the regulated environment surrounding initial coin offerings (ICOs). An ICO is a call for funding to raise funds through a blockchain, where cryptoassets are issued. Key stakeholders involved are the firms launching ICOs, the investors, and the financial regulators. We conducted a case study of a firm that launched an ICO, impak Finance, the first regulated ICO in Canada. Based on the interviews of key respondents, we developed a framework identifying the main risks and benefits for firms to performing an ICO, showing differences between unregulated and regulated ICOs. Our study makes a number of research and practical contributions. First, we document the case of the first regulated ICO in Canada. The interviews conducted provided access to privileged insider information. Second, very few studies have been conducted on the impact of blockchains as a financing vehicle. ICOs using blockchains may be disruptive not only from a technology standpoint but also from a financial standpoint. While the possible applications of blockchains are unknown to us to date, we do know that blockchains have the potential to challenge the traditional financial system monitored by financial regulators. Last, the study identifies, through a framework, the risks and benefits of performing an ICO in an unregulated versus a regulated context, which has practical implications for firms operating in the fintech space. We trust that this framework will be useful for firms using ICOs, for investors, and for financial regulators.
Tracing Mineralogy and Alteration Intensity Using the Spectral Alteration Index and Depth Ratios at the Northwest Zone of the Lemarchant Volcanogenic Massive Sulfide Deposit, Newfoundland, Canada

Abstract The use of hyperspectral reflectance in mineral exploration has been steadily increasing in recent decades. This study presents a novel approach that integrates geochemical and spectral proxies to delineate ore formation and alteration processes, which provide new spectral-based exploration parameters that can be used in real time. The precious metal-bearing, bimodal-felsic Northwest zone of the Lemarchant volcanogenic massive sulfide (VMS) deposits, Newfoundland, Canada, is used as a case study. Alteration associated with the Northwest zone includes intense and localized sulfide (pyrite, chalcopyrite, sphalerite, and galena) and barite enrichment, as well as quartz, white mica, and chlorite alteration. Zones of elevated Zn (&amp;gt;5,000 ppm) are associated with high chlorite carbonate pyrite index (CCPI), Ishikawa alteration index (AI), Ba/Sr, and low Na2O values and elevated SiO2 and K2O, Fe2O3, Na2O, and BaO contents, similar to global alteration signatures in VMS deposits. Mineralized areas contain phengitic white micas with 2,200-nm absorption features longer than 2,215 nm and Mg-rich chlorites with 2,250-nm absorption features shorter than 2,252 nm. Together, these data are consistent with the Northwest zone having undergone intense hydrothermal alteration during the mineralization event. A new lithology-normalized spectral alteration index (SAI) for white mica and chlorite was developed in order to map and characterize the alteration intensity surrounding the deposit. In addition, depth ratio parameters (2200D/2340D vs. 2250D/2340D) were used to characterize mineralogical changes and zonation. Together, these features document a paleofluid pathway with Mg chlorite alteration extending to at least 300 m away from the mineralization, outside the study area, within the andesitic and dacitic units. The use of hyperspectral reflectance coupled with geochemical alteration proxies permitted the identification of areas of intense alteration, the chemical affinities of the minerals, and their relationships to alteration processes (i.e., seawater alteration versus silicification), which would not be possible using geochemistry alone.
EPISTEMIC MULTILATERAL LOGIC

Abstract We present epistemic multilateral logic , a general logical framework for reasoning involving epistemic modality. Standard bilateral systems use propositional formulae marked with signs for assertion and rejection. Epistemic multilateral logic extends standard bilateral systems with a sign for the speech act of weak assertion (Incurvati &amp; Schlöder, 2019) and an operator for epistemic modality. We prove that epistemic multilateral logic is sound and complete with respect to the modal logic $\mathbf {S5}$ modulo an appropriate translation. The logical framework developed provides the basis for a novel, proof-theoretic approach to the study of epistemic modality. To demonstrate the fruitfulness of the approach, we show how the framework allows us to reconcile classical logic with the contradictoriness of so-called Yalcin sentences and to distinguish between various inference patterns on the basis of the epistemic properties they preserve.
A Comprehensive Theory and Variational Framework for Anti‐aliasing Sampling Patterns

Abstract In this paper, we provide a comprehensive theory of anti‐aliasing sampling patterns that explains and revises known results, and introduce a variational optimization framework to generate point patterns with any desired power spectra and anti‐aliasing properties. We start by deriving the exact spectral expression for expected error in reconstructing a function in terms of power spectra of sampling patterns, and analyzing how the shape of power spectra is related to anti‐aliasing properties. Based on this analysis, we then formulate the problem of generating anti‐aliasing sampling patterns as constrained variational optimization on power spectra. This allows us to not rely on any parametric form, and thus explore the whole space of realizable spectra. We show that the resulting optimized sampling patterns lead to reconstructions with less visible aliasing artifacts, while keeping low frequencies as clean as possible. Although we focus on image plane sampling, our theory and algorithms apply in any dimensions, and the variational optimization framework can be utilized in all problems where point pattern characteristics are given or optimized.
Detection of Text Similarity for Indication Plagiarism Using Winnowing Algorithm Based K-gram and Jaccard Coefficient

Abstract One of the digital data is a document. Documents can be easily copied and deleted. Anyone can retype or copy parts of the document. In this paper will detect text similarity. The more similarity of words there is the more indicated the document is plagiarism. Winnowing algorithm performs the calculation of hash values of each k-gram. This method improves the search time with more accuracy in the detection process. All data selected hash values will be fingerprints of a document. Fingerprint will be used as a basis for comparing similarities between text data. The fingerprint value of the winnowing process for each document will be matched by using the Jaccard Coefficient to measure the similarity of the text. In this paper results show that the adjustment of the k-gram and window values can affect the final result of the similarity percentage value. The smaller the k-gram value, the greater the percentage value.
How to carry out conceptual properties norming studies as parameter estimation studies: Lessons from ecology

Conceptual properties norming studies (CPNs) ask participants to produce properties that describe concepts. From that data, different metrics may be computed (e.g., semantic richness, similarity measures), which are then used in studying concepts and as a source of carefully controlled stimuli for experimentation. Notwithstanding those metrics’ demonstrated usefulness, researchers have customarily overlooked that they are only point estimates of the true unknown population values, and therefore, only rough approximations. Thus, though research based on CPN data may produce reliable results, those results are likely to be general and coarse-grained. In contrast, we suggest viewing CPNs as parameter estimation procedures, where researchers obtain only estimates of the unknown population parameters. Thus, more specific and fine-grained analyses must consider those parameters’ variability. To this end, we introduce a probabilistic model from the field of ecology. Its related statistical expressions can be applied to compute estimates of CPNs’ parameters and their corresponding variances. Furthermore, those expressions can be used to guide the sampling process. The traditional practice in CPN studies is to use the same number of participants across concepts, intuitively believing that practice will render the computed metrics comparable across concepts and CPNs. In contrast, the current work shows why an equal number of participants per concept is generally not desirable. Using CPN data, we show how to use the equations and discuss how they may allow more reasonable analyses and comparisons of parameter values among different concepts in a CPN, and across different CPNs.