Reviewerly/exHarmony huggingface dataset:
Relational social recommendation: Application to the academic domain

This paper outlines RSR, a relational social recommendation approach applied to a social graph comprised of relational entity profiles. RSR uses information extraction and learning methods to obtain relational facts about persons of interest from the Web, and generates an associative entity-relation social network from their extracted personal profiles. As a case study, we consider the task of peer recommendation at scientific conferences. Given a social graph of scholars, RSR employs graph similarity measures to rank conference participants by their relatedness to a user. Unlike other recommender systems that perform social rankings, RSR provides the user with detailed supporting explanations in the form of relational connecting paths. In a set of user studies, we collected feedbacks from participants onsite of scientific conferences, pertaining to RSR quality of recommendations and explanations. The feedbacks indicate that users appreciate and benefit from RSR explainability features. The feedbacks further indicate on recommendation serendipity using RSR, having it recommend persons of interest who are not apriori known to the user, oftentimes exposing surprising inter-personal associations. Finally, we outline and assess potential gains in recommendation relevance and serendipity using path-based relational learning within RSR.
Activities Recognition and Fall Detection in Continuous Data Streams Using Radar Sensor

This student paper presents a Quadratic-kernel Support Vector Machine (SVM) based FMCW (Frequency Modulated Continuous Wave) radar system to recognize daily activities and detect fall accidents. Data collected in this work is divided into two different collection modes, namely, snapshots mode (different activities individually collected in isolation) and continuous activity mode (continuous streams of activities collected one after the other). For the continuous activity streams, a sliding window approach with 4s duration and 70% overlapping has achieved 84.7% classification accuracy and subsequent improvement of 2.6% has been proved by using Sequential Forward Selection (SFS) on six participants to identify an optimal feature set. A `tracking' graph has been utilized to verify that the radar system can correctly identify falls as critical events among the other activities.
Cascaded Static and Dynamic Local Feature Extractions for Face Sketch to Photo Matching

The automatic identification of a corresponding photo from a face sketch can assist in criminal investigations.The face sketch is rendered based on the descriptions elicited by the eyewitness.This may cause the face sketch to have some degrees of shape exaggeration that make some parts of the face geometrically misaligned.In this paper, we attempt to address the effect of these influences by a cascaded static and dynamic local feature extraction method so that the constructed feature vectors are built based on the correct patches.In the proposed method, the feature vectors from the local static extraction on a sketch and photo are matched using the nearest neighbors.Then, some n most similar photos are shortlisted based on the nearest neighbors.These photos are eventually re-matched using feature vectors from the local dynamic extraction method.The feature vectors are matched using the L 1 -distance measure.The experimental results for The Chinese University of Hong Kong (CUHK) Face Sketch Database (CUFS) and CUHK Face Sketch FERET Database (CUFSF) datasets indicate that the proposed method outperforms the state-of-the-art methods.
Distributional semantics of objects in visual scenes in comparison to text

The distributional hypothesis states that the meaning of a concept is defined through the contexts it occurs in. In practice, often word co-occurrence and proximity are analyzed in text corpora for a given word to obtain a real-valued semantic word vector, which is taken to (at least partially) encode the meaning of this word. Here we transfer this idea from text to images, where pre-assigned labels of other objects or activations of convolutional neural networks serve as context. We propose a simple algorithm that extracts and processes object contexts from an image database and yields semantic vectors for objects. We show empirically that these representations exhibit on par performance with state-of-the-art distributional models over a set of conventional objects. For this we employ well-known word benchmarks in addition to a newly proposed object-centric benchmark.
Meta-omics data and collection objects (MOD-CO): a conceptual schema and data model for processing sample data in meta-omics research

With the advent of advanced molecular meta-omics techniques and methods, a new era commenced for analysing and characterizing historic collection specimens, as well as recently collected environmental samples. Nucleic acid and protein sequencing-based analyses are increasingly applied to determine the origin, identity and traits of environmental (biological) objects and organisms. In this context, the need for new data structures is evident and former approaches for data processing need to be expanded according to the new meta-omics techniques and operational standards. Existing schemas and community standards in the biodiversity and molecular domain concentrate on terms important for data exchange and publication. Detailed operational aspects of origin and laboratory as well as object and data management issues are frequently neglected. Meta-omics Data and Collection Objects (MOD-CO) has therefore been set up as a new schema for meta-omics research, with a hierarchical organization of the concepts describing collection samples, as well as products and data objects being generated during operational workflows. It is focussed on object trait descriptions as well as on operational aspects and thereby may serve as a backbone for R&D laboratory information management systems with functions of an electronic laboratory notebook. The schema in its current version 1.0 includes 653 concepts and 1810 predefined concept values, being equivalent to descriptors and descriptor states, respectively. It is published in several representations, like a Semantic Media Wiki publication with 2463 interlinked Wiki pages for concepts and concept values, being grouped in 37 concept collections and subcollections. The SQL database application DiversityDescriptions, a generic tool for maintaining descriptive data and schemas, has been applied for setting up and testing MOD-CO and for concept mapping on elements of corresponding schemas.
Deep Learning–Based Multimedia Analytics

The multimedia community has witnessed the rise of deep learning–based techniques in analyzing multimedia content more effectively. In the past decade, the convergence of deep-learning and multimedia analytics has boosted the performance of several traditional tasks, such as classification, detection, and regression, and has also fundamentally changed the landscape of several relatively new areas, such as semantic segmentation, captioning, and content generation. This article aims to review the development path of major tasks in multimedia analytics and take a look into future directions. We start by summarizing the fundamental deep techniques related to multimedia analytics, especially in the visual domain, and then review representative high-level tasks powered by recent advances. Moreover, the performance review of popular benchmarks gives a pathway to technology advancement and helps identify both milestone works and future directions.
Inferring Search Queries from Web Documents via a Graph-Augmented Sequence to Attention Network

We study the problem of search query inference from web documents, where a short, comprehensive natural language query is inferred from a long article. Search query generation or inference is of great value to search engines and recommenders in terms of locating potential target users and ranking content. Despite being closely related to other NLP tasks like abstract generation and keyword extraction, we point out that search query inference is, in fact, a new problem, in that the generated natural language query, which consists of a few words, is expected to be comprehensive enough to lead to the click-through of the corresponding document. Therefore, query generation requires an accurate inference of query words, as well as a deeper level of understanding on document semantic structures. Toward this end, we propose a novel generative model called the Graph-augmented Sequence to Attention (G-S2A) network. Adopting an Encoder-Decoder architecture, G-S2A incorporates a sentence-level Graph Convolutional Network (GCN), a keyword-level GCN, as well as a hierarchical recurrent neural network (RNN) into the encoder to generate structural document representations. An attentional Transformer decoder is then applied to combine different types of encoded features to generate a target query. On a query-document dataset from a real-world search engine, our model outperforms several neural generative models on a wide range of metrics.
Semantic Segmentation of Fisheye Images

Semantic segmentation of fisheye images (e.g., from action-cameras or smartphones) requires different training approaches and data than those of rectilinear images obtained using central projection. The shape of objects is distorted depending on the distance between the principal point and the object position in the image. Therefore, classical semantic segmentation approaches fall short in terms of performance compared to rectilinear data. A potential solution to this problem is the recording and annotation of a new dataset, however this is expensive and tedious. In this study, an alternative approach that modifies the augmentation stage of deep learning training to re-use rectilinear training data is presented. In this way we obtain a considerably higher semantic segmentation performance on the fisheye images: +18.3% intersection over union (IoU) for action-camera test images, +8.3% IoU for artificially generated fisheye data, and +18.0% IoU for challenging security scenes acquired in bird's eye view.
Rapidly exploring Random Tree* with a sampling method based on Sukharev grids and convex vertices of safety hulls of obstacles

The path planning for an Unmanned Aerial Vehicles ensures that a dynamically feasible and collision-free path is planned between a start and an end point within a navigation environment. One of the most used algorithms for path planning is the Rapidly exploring Random Tree, where each one of its nodes is randomly collected from the navigation environment until the start and end navigation points are connected through them. The Rapidly exploring Random Tree algorithm is probabilistically complete, which ensures that a path, if one exists, will be found if the quantity of sampled nodes increases infinitely. However, there is no guarantee that the shortest path to a navigation environment will be planned by Rapidly exploring Random Tree algorithm. The Rapidly exploring Random Tree* algorithm is a path planning method that guarantees the shorter path length to the UAV but at a high computational cost. Some authors state that by informing sample collection to specific positions on the navigation environment, it would be possible to improve the planning time of this algorithm, as example of the Rapidly exploring Random Tree*-Smart algorithm, that utilize intelligent sampling and path optimization procedures to this purpose. This article introduces a novel Rapidly exploring Random Tree*-based algorithm, where a new sampling process based on Sukharev grids and convex vertices of the security hulls of obstacles is proposed. Computational tests are performed to verify that the new sampling strategy improves the planning time of Rapidly exploring Random Tree*, which can be applied to real-time navigation of Unmanned Aerial Vehicles. The results presented indicate that the use of convex vertices and grid of Sukharev accelerate the planning time of the Rapidly exploring Random Tree* and show better performance than the Rapidly exploring Random Tree*-Smart algorithm in several navigation environments with different quantities and spatial distributions of polygonal obstacles.
Challenges in LIS Education in China and the United States

For decades, library and information science (LIS) education has been producing LIS professionals to serve people in all walks of life. But there are challenges to LIS education as society advances. This study performed quantitative and qualitative content analyses of data collected about LIS education in China and the United States. Four categories of challenges in LIS education are identified: (1) identity and accreditation, (2) survival and thriving, (3) curriculum update and enhancement, and (4) course delivery format and content. The challenges that each country encounters in LIS, as expected, are not the same, although some appear similar. All the challenges are discussed and contrasted in the context of each country’s traditions and practices in LIS education. Some suggestions are also made regarding how to successfully meet the challenges this study has explored. The findings of this study can help all constituencies (i.e., educators, practitioners, and students) to better understand the challenges of LIS education in China and the United States so that feasible measures can be developed to meet them. In addition, each country can benefit from this study by learning from what its counterpart has done in LIS education with regard to barriers and challenges.
Large-scale interactive numerical experiments of chaos, solitons and fractals in real time via GPU in a web browser

The study of complex systems has emerged as an important field with many discoveries still to be made. Computer simulation and visualization provide important tools for studying complex dynamics including chaos, solitons, and fractals, but available computing power has been a limiting factor. In this work, we describe a novel and highly efficient computing and visualization paradigm using a Web Graphics Library (WebGL 2.0) methodology along with our newly developed library (Abubu.js). Our approach harnesses the power of widely available and highly parallel graphics cards while maintaining ease of use by simplifying programming through hiding implementation details, running in a web browser without the need for compilation, and avoiding the use of plugins. At the same time, it allows for interactivity, such as changing parameter values on the fly, and its computing is so fast that zooming in on a region of a fractal like the Mandelbrot set can incur no delay despite having to recalculate values for the entire plane. We demonstrate our approach using a wide range of complex systems that display dynamics from fractals to standing and propagating waves in 1, 2 and 3 dimensions. We also include some models with instabilities that can lead to chaotic dynamics. For all the examples shown here we provide links to the codes for anyone to use, modify and further develop with other models. Overall, the enhanced visualization and computation capabilities provided by WebGL together with Abubu.js have great potential to facilitate new discoveries about complex systems.
Semantic Bridging of Cultural Heritage Disciplines and Tasks

The Cultural Heritage (CH) domain encompasses a wide range of different disciplines, serving the study, interpretation, curation, and preservation of objects, collections, archives, sites, and the dissemination of related knowledge. In this context, stakeholders generate, retrieve, and share a vast amount of diverse information. Therefore, information interoperability has been considered a crucial task, especially in terms of semantics. In this way, the CIDOC CRM (International Committee for Documentation Conceptual Reference Model) has been widely used as an underlying model that offers interoperability between CH domain metadata standards and ontologies. To the best of our knowledge, an overall review of mapping, merging, and extending this core ontology, as well as an aggregate table which classifies and correlates those ontologies and standards, has not yet been presented. Our study conducts an aggregate review of relevant published efforts and outlines the various associations between them, encapsulating the CIDOC CRM and its specialized models, as well. This work aims to further clarify the field and scope of the different works, identify their methods, and highlight the semantic overlap, or differences, between them.
Comparison and Efficacy of Synergistic Intelligent Tutoring Systems with Human Physiological Response

The analysis of physiological signals is ubiquitous in health and medical diagnosis as a primary tool for investigation and inquiry. Physiological signals are now being widely used for psychological and social fields. They have found promising application in the field of computer-based learning and tutoring. Intelligent Tutoring Systems (ITS) is a fast-paced growing field which deals with the design and implementation of customized computer-based instruction and feedback methods without human intervention. This paper introduces the key concepts and motivations behind the use of physiological signals. It presents a detailed discussion and experimental comparison of ITS. The synergism of ITS and physiological signals in automated tutoring systems adapted to the learner's emotions and mental states are presented and compared. The insights are developed, and details are presented. The accuracy and classification methods of existing systems are highlighted as key areas of improvement. High-precision measurement systems and neural networks for machine-learning classification are deemed prospective directions for future improvements to existing systems.
Improving Arabic Text to Image Mapping Using a Robust Machine Learning Technique

In this paper, we introduce an approach to automatically convert simple modern standard Arabic children's stories to the best representative images that can efficiently illustrate the meaning of words.It is a kind of imitating the imaginative process when children read a story, yet a great challenge for a machine to achieve it.For simplification issues, we apply several techniques to find the images and we associate them with related words dynamically.First, we apply natural language processing techniques to analyze the text in stories and we extract keywords of all characters and events in each sentence.Second, we apply an image captioning process through a pre-trained deep learning model for all retrieved images from our multimedia database as well as the Google search engine.Third, using sentence similarities, most significant images are retrieved back by selecting top-k highest similarity values.It is worth mentioning that using the captioning process, to rank top-k images, has shown reasonable precision values as per our preliminary results.The option to refine or validate the ranked images to compose the final visualization for each story is also provided to ensure a flexible and safe learning environment.
Systematic composition of independent language features

Systematic reuse is crucial to efficiently engineer and deploy software languages to software experts and domain experts alike. But “software languages are software too”, and hence their engineering, customization, and reuse are subject to similar challenges. To this effect, we propose an approach for composing independent, grammar-based language syntax modules in a structured way that realizes a separation of concerns among the participants in the life cycle of the languages. We present a refined concept of systematic and controlled syntactic variability of extensible software language product lines through identification of syntax variation points and derivation of variants from independently developed features. This facilitates reuse of software languages and reduces the efforts of engineering and customizing languages for specific domains. We realized our concept with the MontiCore language workbench and assessed it through a case study on architecture description languages. Ultimately, systematic and controlled software language reuse reduces the effort of software language engineering and fosters the applicability of software languages.
Noise Removal Through the Exploration of Subjective and Apparent Denoised Patches Using Discrete Wavelet Transform

The effective elimination of noise from the original image has emerged as a hard nut to crack for the researchers. The hunt for effective image denoising techniques continues to be a strenuous task at the cross junction of the functional investigation and statistics. Though there are many approaches to image denoising, the performances of them are comparatively low. In this paper, the internal and external data cubes are smartly configured so as to arrive at the identical patches from the respective noise-contaminated and web images. In this regard, there are two phases employing diverse filtering methods intended for the reduction of noise. In the initial phase, a graph-based optimization technique is introduced to effectively enhance the patch harmonizing in the external denoising. In the second stage, the noise is significantly scaled down by removing the respective internal and external cubes. Discrete Wavelet Transform (DWT) filtering approach is utilized for achieving the superlative precision in denoising image in relation to the existing filters.
THE EFFECTIVENESS OF GEOGEBRA WHEN TEACHING LINEAR FUNCTIONS USING THE IPAD

There is concern about mathematics success and its related pedagogy. Society has seen rapid changes in the economy and technology with a call for these changes to be reflected within the classroom. New methods of teaching mathematics are being sought with the purpose to improve teaching and learning while making mathematics relatable to the new generation of learners. The incorporation of technology within the classroom has been seen an option to make this change. The purpose of this research was to determine how effective the use of the GeoGebra app is in allowing learners to successfully discover the properties of straight line graphs. Furthermore, the research looked at learner responses to using the app. A qualitative research design was used with data generated through a task-based investigation, as well as individual interviews. Results of the research showed that the use of GeoGebra aided learners successfully in discovering the properties of straight-line graphs with the majority of learners understanding both concepts. The results also showed that learners had a positive outlook to the use of the app and enjoyed the experience. Keywords: GeoGebra, iPad technology, mathematics teaching, linear functions, software manipulation.