Reviewerly/exHarmony huggingface dataset:
Anisotropic Diffusion-based Enhancement of Old Historical Photos related to Romanian Centenary

This paper is dedicated to the Centenary of Great Romanian Union, disseminating an image enhancement technique that is able to restore properly the old pictures printed in the interwar period and describing World War I events. The proposed restoration technique is based on a nonlinear second-order anisotropic diffusion model that removes effectively the additive and multiplicative noise, and also overcomes the unintended effects, while preserving the photo's details. A numerical approximation scheme is constructed for solving this PDE-based model and many results of our tests are provided.
On Open Access to Research Data: Experiences and reflections from DANS

In this article we are concerned with the question of what open access means in relation to research data. To that end we will (1) briefly discuss the definition of open access and the most important expected benefits; (2) highlight the publication - data distinction and the challenges regarding data; (3) discuss research data and open access in terms of its focus, licensing, and current practices at DANS in publishing data sets open access.
Web of Things Semantic Functionality Distance

The Web of Things is an architectural initiative proposed by the World Wide Web Consortium, to enable Internet of Things devices to interact through Web standards. One of the cornerstones of the architecture is a Thing Description, which is an object model that exposes devices to the Internet through a common interface composed by properties, actions and events. In this paper, we evaluate the similarity level on capabilities calculated for Web of Things objects. We developed, based on the Thing Description, a metric called Web of Things Semantic Functionality Distance (WoTSFD). The semantic functionality distance is a measure of the device ability to perform the same function in a specific application context. We evaluate this metric in a smart home environment. The results show that different devices can be detected to be similar, thus suitable to collaborate or be replaced by each other to perform a specific task in a determined use case.
Pedagogical bases of innovation activities in higher education institutions

This article outlines the issues of introducing innovative approaches to improving learning processes. There are also recommendations for the development of high pedagogical technologies. The plan gives us a project of the future, based on our understanding of the possibilities of its implementation and the needs for it. The forecast is intended to give an objective picture of the future, based on a scientific analysis of the natural historical process of the development of society. The forecast should reflect not only today's ideas about the organization and methods of functioning of systems and innovation activities, but also those changes (development) in this area that are almost imperceptible today. These paths are not so diverse, and therefore the emerging forecast scenarios differ mainly in what resources can be attracted for a radical improvement and development of the republic's innovation activity. From the point of view of the organization, this means the need to involve these bodies in making forecasts for the development of sectoral territorial innovation projects. Therefore, forecasting the development and regulation of innovation activity throughout the Republic turns out to be important organizational and methodological issues that should take their rightful place. This shows a special place of prognostic studies in the development and regulation of innovation activities. They are based on the scientific results achieved and prove to be the level of these results.
Towards an artifact to support agile teams in software analytics activities

Software analytics supports data-driven decision making, which allows software practitioners to leverage valuable insights from software data to improve their processes and many quality aspects of the software.In this paper, we present an artifact designed from a set of patterns to support agile teams to plan and manage software analysis activities, named Software Analytics Canvas.Further, we report the study undertaken to evaluate the ease of use and the utility of our canvas from the practitioners' viewpoint, and a participatory design session carried out to collect information about possible artifact improvements.In general, subjects found the artifact useful, but some of them reported difficulties in learning and understanding how to use it.In the participatory design, they pointed out improvement points and a new layout for the canvas components.The results of both studies helped us refine the proposed artifact, improving both the terms used in each element and the layout of the blocks to make more sense for its users.
Did university fail to protect student from peer harassment?

When the plaintiff, a Taylor University student, resided in one of the residence halls during the 2014–15 academic year, he allegedly once saw a fellow resident streak naked through the lobby and once witnessed him shout “penis, penis” on a crowded airplane. He also purportedly saw: (1) a video the fellow resident had posted online, in which it appeared he had killed someone; and (2) videos of the same student with some naked girls doing “all kinds of raunchy and inappropriate stuff in the dorm room.”
iCare Designer: A Rule-Driven Layout Co-Designing System for Elderly Caring

In CSCW and related fields, computer supported collaborative design has been used in various fields. Yet, there has been little research focusing on the collaborative context design for elderly caring. For filling this gap, this paper explores a notion of rule-driven layout co-designing system for elderly caring. What makes it distinct from other layout designing system is that it employs hierarchically modularization to store all standardized components that are needed for the aging caring space, and embeds intelligence algorithm into the typical evidence-based design for improving design efficiency and reducing design costs. In this paper, we designed and implemented a prototype called iCare Designer, and conducted a preliminary study to understand how this notion works in reality. While findings of the preliminary study suggest promises of the notion of rule-driven space layout co-designing system, it reveals some challenges this kind of systems to truly work in practices, mainly reflecting in its flexibility and perception to ambient environment. An in-depth discussion is provided in the end.
PROCESS ORIENTATION IN THE MODERN CONTROLLING

The controlling is a combination of the target-oriented control activity, the used methods and other soft factors as well. The controlling is constantly changing because it always must meet the current challenges. Each company receives and generates a lot of data, Big Data and Data Mining help in predictive, secure and user-friendly analysis. One of the most important trend is the Industry 4.0 and the compliance to the Digital Business Models through intelligent networks and Cyber-Physical Systems. The controller is responsible for identifying and evaluating the business changes and requirements; so he will support the management in the implementation of it. We are reviewing the importance of the Industry 4.0 and how have to develop the process orientation of controlling at the modern Digital Business Models. In the paper, we will illustrate how can be achieved it by taking into account the Industry 4.0 strategy and how digitalisation can help.
Unsupervised classification and automated shape recognition as tool for computer-assisted reproducible typology

Since 2016, in the SNFS-project ‘Mobilities, entanglements and transformations in Neolithic societies on the Swiss Plateau (3900-3500 BC)’, we have been using a computer-aided classification of vessel shapes to investigate completely preserved Neolithic pottery of Swiss wetland sites and neighbouring regions from the 4th millennium BC beyond classical typologies and cultural assignments. Our strategy is essentially very simple: We follow a holistic approach in which the entire vessel body is understood as a rotational body (comparable with Mom 2005, Chapman et al., 2006, Keogh et al., 2009). One side of the rasterized, filled and equally scaled profile is extracted, and via a simple transfer of the image information into a matrix, the profile line is converted into 400 measuring points. The profile information obtained in this way, enriched with nominal values such as rim shape or decoration as well as other metric values such as absolute height, can be evaluated using various multivariate methods. We opted for a combination of t-sne as ordination and dimension-reducing method and HDBSCAN as cluster algorithm for this analysis. This computer-aided method was accompanied by a impressionistic classification by hand. In our analyses it became clear that both methods complement each other meaningfully: While computer-aided classification was able to work out more general, cross-cultural trends, which can be interpreted with regard to the function of the vessels and a consumer perspective, impressionistic classification leads to an identification of different styles, which rather open up a producer perspective. The juxtaposition of both methods on the same material can be used to overcome existing stereotypes and topoi in the ceramic classification. Furthermore the results also serve to identify yet unresolved shortcomings of the computer-based approach, which will be addressed in the future.
Multi-node activity recognition based on wireless sensor network in classroom

This work constructs and discusses a multi-node wireless activity recognition system for teaching environments. The system can collect behavior data simultaneously from multiple users overall wearing the wireless sensor. Behavior data is collected and calculated from users to extract features. The features are then used to do the effective classifier training and recognize different activities. As introduced in classroom teaching, this system can provide high recognition accuracy of the student's activity and give teachers helpful information to better adjust their course. It is 94.092% in recognition accuracy as being used to recognize five daily activities of learners in classroom education.
A new trademark detection method via trademark confidence score of MSERs

This paper proposes a new algorithm to provide high quality potential trademark locations for trademark detection. In real-world circumstances, trademark regions often possess some distinctive, invariant and stable properties which can be gained effectively and efficiently by Maximally Stable Extremal Regions (MSERs). Based on this observation, we design Trademark Confidence Score (TCS) for adaptive MSERs in the images. Then a window refinement algorithm is proposed to retain the high-quality candidate windows generated by Selective Search (SS). Experiments on FlickerLogos-27 and our own dataset demonstrate that our algorithm can significantly reduce the number of candidate proposals produced by SS with little sacrifice of recall for trademarks. Moreover, for trademark detection, our algorithm has better performance while reducing the computational cost of detection.
Title Correction: Using a Call Center to Reduce Harm From Self-Administration of Reproductive Health Medicines in Bangladesh: Interrupted Time-Series

[This corrects the article DOI: 10.2196/12233.].
Simple Triangulation for Asynchronous Stereo Cameras

We propose a method to measure a three-dimensional (3D) trajectory using asynchronous stationary stereo cameras placed such that each camera is in the other's view angle. Time shift estimation between the two image sequences is performed by minimizing the sum of squares of distance between the two lines of sight from stereo cameras in the frame unit and further in subframe. The 3D position of the target is obtainable using an interpolated target image position at the estimated subframe time. The method enables the use of commercially available digital cameras in an outdoor environment. Furthermore, it requires no impractically large calibration target or any estimation of the fundamental matrix.
Momentum-Based Topology Estimation of Articulated Objects

Articulated objects like doors, drawers, valves, and tools are pervasive in our everyday unstructured dynamic environments. Articulation models describe the joint nature between the different parts of an articulated object. As most of these objects are passive, a robot has to interact with them to infer all the articulation models to understand the object topology. We present a general algorithm to estimate the inherent articulation models by exploiting the momentum of the articulated system along with the interaction wrench while manipulating the object. We validate our approach with experiments in a simulation environment.
Integrated Database System with Spatial Information for Disaster Risk Management

Despite availability of various image sources for specific areas, a new disaster management system is likely to be implemented by using only one of them. Thus, its applicability and ex ...
Accurate CT-ultrasound image registration using simulated transformation optimization

The accuracy of ultrasound/computed tomography (CT) image registration is the key to ultrasound-guided intervention. Thus, the aim of this study is to address the limitation of current image similarity measures to evaluate the accuracy of ultrasound/CT image registration correctly. In this study, an ultrasound/CT image registration method based on simulated transformation optimization is presented. The approach initially preprocesses the ultrasound/CT images for registration through the tensor principal component analysis method to reduce the influence of noise on registration accuracy. Multiscale enhancement algorithm is also adopted to enhance the tubular structures of the CT images. Simulated transformation optimization based on the CT images is then provided. Afterward, given the ultrasonic imaging parameter estimates, the method captures a CT section to obtain ultrasonic images by simulation. The ultrasonic simulation is introduced into the image similarity measure, and the simulation transformation correlation measure is established. The transformation matrix is optimized by the conjugate direction acceleration algorithm to realize the fast and accurate registration of the ultrasound/CT image. Experimental results demonstrate that when Correlation of Simulation Transformation is employed as the similarity measure, the variation range of the six parameters in the transformation matrix is ±0.01, and the ultrasound/CT image registration method based on simulated transformation optimization can rapidly and accurately register ultrasound/CT images. The accurate registration of ultrasound/CT images enables the combination between real-time ultrasonic images and preoperative CT images. Hence, it has the potential to be utilized for ultrasound-guided surgical navigation in clinical practice.
Surface Vessel Tracking in Airborne Infrared Imagery

Visual target tracking has been studied for decades and still remains a challenging problem. Ship tracking on infrared images has numerous challenges compared to conventional target tracking such as fast changing of appearance. Rapid appearance change caused by the manoeuvring movement of the target of image acquiring platform, confusion and occlusion caused by the active countermeasures employed by the target and disguise by cooling systems causes the target tracking algorithms to have low performance. In this work, a convolutional neural network and correlation filter based algorithm which tracks surface vessels on infrared imagery is proposed. Performance of the proposed algorithm is compared against the distinguished, popular target tracking algorithms from the literature. Performances are evaluated on a specially created infrared ship images dataset.
The X Factor in Understanding Listening Comprehension Skill

This research was aimed to find out factors affecting students’ challenges in listening comprehension as perceived by students at SMAN 1 Calang. This is a descriptive qualitative study. The samples of this research were selected by using purposive sampling. There were 75 eleventh-grade students who participated in this research. Questionnaire and interview were designed as research instruments. The questionnaire consisted of 30 items grouped into five categories, namely students’ listening comprehension challenges related to factors attributed to the content and linguistics features of the listening text, factors attributed to the speaker, factors attributed to the listener, and factors attributed to the physical setting. Moreover, the interview consisted of three questions asking students’ opinion about listening comprehension, challenges they faced in listening comprehension activity, and factors that caused those challenges. The result indicates listening texts, speaker, listener, and physical setting were the main sources of students’ listening comprehension challenges. The dominant factor affecting students’ challenges in listening comprehension was the physical setting such as the noise from environmental surroundings, with the percentage of 89.6%.
Learning Individual and Group Preferences in Abstract Argumentation

In Abstract Argumentation, given the same AA framework rational agents accept the same arguments unless they reason by different AA semantics. Real agents may not do so in such situations, and in this paper we assume that this is because they have different preferences over the confronted arguments. Hence by reconstructing their reasoning processes, we can learn their hidden preferences, which then allow us to predict what else they must accept. Concretely we formalize and develop algorithms for such problems as learning the hidden preference relation of an agent from his expressed opinion, by which we mean a subset of arguments or attacks he accepted; and learning the collective preferences of a group from a dataset of individual opinions. A major challenge we addressed in this endeavor is to represent and reason with “answer sets” of preference relations which are generally exponential or even infinite.
Testing Neural Programs.

Deep neural networks have been increasingly used in software engineering and program analysis tasks. They usually take a program and make some predictions about it, e.g., bug prediction. We call these models neural program analyzers. The reliability of neural programs can impact the reliability of the encompassing analyses. In this paper, we describe our ongoing efforts to develop effective techniques for testing neural programs. We discuss the challenges involved in developing such tools and our future plans. In our preliminary experiment on a neural model recently proposed in the literature, we found that the model is very brittle, and simple perturbations in the input can cause the model to make mistakes in its prediction.
StoryMiner: An Automated and Scalable Framework for Story Analysis and Detection from Social Media

Author(s): Shahbazi, Behnam | Advisor(s): Parker, Stott; Roychowdhury, Vwani P | Abstract: The explosive growth of social media over the past decade, together with advancements in computational power, has paved the way for many large-scale sociological studies, which were not possible before. Social media sites are now the primary source of data for much of our insights into society, from trending topics to behavioral patterns of various groups such as online shoppers or political parties. One particular area of interest is the analysis of events and interactions through their descriptions in social media posts. Inferring and analyzing real-world events from social media in a large-scale automated way provides a platform for understanding real-world stories, which are not only influenced by but also heavily impact public opinion. Therefore, it is necessary to design computational and statistical tools to automatically extract social media stories. In this dissertation, we introduce StoryMiner, an automated and scalable machine learning framework rooted in a narrative theory that identifies and tracks multi-scale narrative structures from large-scale social media text.Predicating our work on narrative theory, StoryMiner derives stories and narrative structures by automatically 1) extracting and co-referencing the actants (entities such as people and objects) and their relationships from the text by proposing an Open Information Extraction system, 2) assigning named-entity types and importance scores for entities and relationships using character-level neural language architectures and other traditional machine learning models, 3) making use of context-dependent word embeddings to aggregate actant-relationships and form contextual story graphs in which the nodes are the actants and the edges are their relationships, and 4) enriching the story graphs with additional layers of information such as sentiments or sequence orders of relationships. StoryMiner allows academic and industry researchers to extract structured knowledge from unstructured text to inform practical decisions. To exhibit the benefits of our framework, among the many possible applications we showcase three major use cases: identification of differences in narrative structures between fake and real conspiracies, summarization of user product opinions from tweets, and reconstruction of plot summaries of famous novels from reader reviews on social reading sites such as Goodreads.
Three-dimensional registration and shape reconstruction from depth data without matching: A PDE approach

The widespread availability of depth sensors like the Kinect camera makes it easy to gather three-dimensional (3D) data. However, accurately and efficiently merging large datasets collected from di¤erent views is still a core problem in computer vision. This question is particularly challenging if the relative positions of the views are not known if there are few or no overlapping points, or if there are multiple objects. Here, we develop a method to reconstruct the 3D shapes of objects from depth data taken from different views whose relative positions are not known. Our method does not assume that common points in the views exist nor that the number of objects is known a priori. To reconstruct the shapes, we use partial differential equations (PDE) to compute upper and lower bounds for distance functions, which are solutions of the Eikonal equation constrained by the depth data. To combine various views, we minimize a function that measures the compatibility of relative positions. As we illustrate in several examples, we can reconstruct complex objects, even in the case where multiple views do not overlap, and, therefore, do not have points in common. We present several simulations to illustrate our method including multiple objects, non-convex objects, and complex shapes. Moreover, we present an application of our PDE approach to object classification from depth data.
The Romanian Corpus Annotated with Verbal Multiword Expressions

This paper reports on the Romanian journalistic corpus annotated with verbal multiword expressions following the PARSEME guidelines.The corpus is sentence split, tokenized, part-of-speech tagged, lemmatized, syntactically annotated and verbal multiword expressions are identified and classified.It offers insights into the frequency of such Romanian word combinations and allows for their characterization.We offer data about the types of verbal multiword expressions in the corpus and some of their characteristics, such as internal structure, diversity in the corpus, average length, productivity of the verbs.This is a language resource that is important per se, as well as for the task of automatic multiword expressions identification, which can be further used in other systems.It was already used as training and test material in the shared tasks for the automatic identification of verbal multiword expressions organized by PARSEME.