Reviewerly/exHarmony huggingface dataset:
Investigating immersive collective intelligence

Collective intelligence is generally defined as the emergence and evolution of intelligence derived from the collective and collaborative efforts of several entities; to include humans and (dis)embodied intelligent agents. Recent advances in immersive technology have led to cost-effective tools that allow us to study and replicate interactions in a controlled environment. Combined together, immersive collective intelligence holds the promise of a symbiotic intelligence that could be greater than the sum of the individual parts. For the military, where the decision making process is typically characterized by high-stress and high-consequence, the concept of a distributive, immersive collective intelligence capability is game changing. Commanders and staff will now be able to remotely immerse themselves in their operational environment with subject matter expertise and advanced analytics. This paper presents the initial steps to understanding immersive collective intelligence with a demonstration designed to discern how military intelligence analysts benefit from an immersive data visualization.
Pattern projection in the short-wave infrared (SWIR): accurate, eye-safe 3D shape measurement

3D sensors based on pattern projection are a popular measuring instrument for the three-dimensional acquisition of people, e.g., for identification purposes or for human-machine interaction. State-of-the-art sensors typically project the pattern(s) at a wavelength of 850 or 940 nm. Although illumination at these wavelengths is barely perceptible or completely imperceptible to the human eye, almost 80 or 50% of the incident radiation reaches the retina. In order to make the 3D measurement of faces not only free of disturbance, but also to make it considerably easier to fall below the limits for retinal exposure, the short-wave infrared (SWIR) is well suited. For instance, at a wavelength of 1450 nm, only a negligible portion of the incident radiation hits the lens of the eye, let alone the retina. Since the terrestrial solar spectrum has a minimum in this wavelength range, the susceptibility to natural ambient light is also reduced. Therefore, we have developed an SWIR 3D scanner, which we present and characterize in this article.
Implementation of Search Process for a Content-Based Image Retrieval Application on System on Chip

The amount of multimedia information on digital platforms has been increasing over the years. Social networks and the advancement of technology have been a determining factor for this event. Due to this fact, the organization, qualification and handling of this type of information has become indispensable, as well as assuring the user the quality of the service in content and execution time in the retrieval of information. This paper presents the implementation of the search process of a content-based image retrieval system, using metric spaces to perform the search and recovery of the image. The high level synthesis is used to development the IP block that will carry out the recovery process in the programmable logic. The experiments are performed on a PYNQ-Z1 board from Xilinx© and on a CPU Intel© Core i5 7th generation. The effectiveness of the implementation is supported by the results obtained.
Blockchain and Identity Persistence

The unique value proposition of 'smart contracts' based on blockchain technology is the creation of a permanent public record of agreed-upon transactions that cannot be changed retroactively. Though this is attractive in terms of reducing the potential for fraud, a person entering into a smart contract pre-commits both their current self and their future selves, no matter what changes occur to them or to their circumstances. The advantages of such pre-commitments can be substantial, but even in an age of increasing adoption of distributed ledger technologies, self-reinvention remains important. From a surveillance perspective, it is important to prevent governments from reliably associating a particular cryptoasset transaction with a particular person. For individuals, it is important to preserve the ability to assume new identities both formally and informally.This book chapter encourages an expanded and refined understanding of what it means for a blockchain use case to "protect privacy," and in particular, how such use cases can encourage a notion of personal identity that is inflexible and matches poorly with individuals' notions regarding their identities. We review several proposed use cases (marriage, money laundering prevention and criminal justice record-keeping) that present this problem in an especially acute form, and highlight good practices to preserve space for self-reinvention.
Interactive SQL Learning Tool with Automated Grading using MySQL Sandbox

This research presents an original tool for automatic grading of SQL statements using MySQL Sandbox[1]. This tool allows students to create and execute the queries in real time. There are many ways to write accurately SQL statement for one question. This is a pain point and burden to the instructors. The existing automated SQL grading algorithms have limitations on supportable statements and understandable error notification. The existing automated SQL grading tool did not support grading on data manipulation language statements i.e. INSERT, UPDATE and DELETE and do not support data definition language commands also; i.e. CREATE, ALTER and DROP. This paper presented an original algorithm which can support these commands. The proposed algorithm can analyze and display the differences between student's answer and correct answer. This algorithm can reduce the instructors' burdens by automatic scoring students' answers and the results directly notify back to both instructors and students. The students can learn and improve themselves faster and effectively.
Unsound Deductions in Early Modern Attribution: The Case of Thomas Watson

Gary Taylor can rightly be called one of the most influential scholars in early modern authorship attribution studies, and his contributions to that field over the years cannot be overstated. Howev...
Templates Framework for the Augmented Catalog System

A novel system is presented for the augmentation of meta-contents, implemented over the existing virtual augmented catalog system (ACS), that shows positive results on improving the usability and lowering the entry barrier for non-expert users. It is introduced the concept of template of augmented reality, which allows the definition of amount and types of digital contents to be augmented together with their geometric transformations and visualization order. Once applied over a virtual catalog, the template helps to maintain a uniform format between its elements while simplifying the task for non-expert users by incorporating part of their domain vocabulary into the interface.
Classification of opinions in cross domains involving emotive values

It is increasingly common for internet users to have access to blogs and social networks, and common for them to express opinions on such sites. This research work is framed within the scope of opinion mining. Opinions allow us to measure people’s perception of a specific topic or product. Knowing the opinion that a person has towards a product or service is of great help for decision making, since it allows, between other things, that potential consumers to verify the quality of the product or service before using it. This research work is framed within the scope of opinion mining. When the number of opinions is very large the analysis gets more complicated and generally resort to tools that allow this task to be performed automatically are sought out. The present work performs an automatic categorization of textual opinions corresponding to four products: books, DVDs, kitchens, and electronics. Both negative and positive opinions are considered for the experiment. Further categorization experiments are performed using different domains of learning. The basic idea is to investigate if we can undertake classification of opinions, positive and negative, of any given domain using instances of training from a different domain. Results obtained from different methods of learning are presented. The results obtained allow us to examine the feasibility of the proposed methodology.
Performant Peer Review for Design Science Manuscripts: A Pilot Study on Dedicated Highlighters

Peer review is under pressure. Demand for reviews is outstripping supply where reviewers tend to be busy people who contribute voluntarily. Authors highly value reviews, yet complain about the time it takes to get feedback to the point of putting research timeliness at stake. Though part of the review process has been moved to the Web, the review itself is still often conducted with the only help of a yellow highlighter, physical or digital. This work looks for more performant highlighters that account for the review specifics. Peer review does not stop at spotting the manuscript (de)merits, it also strives for manuscript improvement and gatekeeping. These functions are conducted within an often tacit research-quality framework, and frequently in a discontinuous way. Unfortunately, when it comes to support review practices, current facilities fall short. This work introduces a set of requirements for review-dedicated highlighters. These requirements are instantiated and evaluated through Review&Go, a color-coding highlighter that generates a review draft out of the reviewer’s highlighting activities. The aim is to offer representational guidance to enhance context/cognitive awareness so that reviewers can exert less effort while offering valuable and timely reviews.
Background Modeling Using Deep-Variational Autoencoder

A foreground segmentation algorithm using the variational autoencoder with skip architecture is presented in this paper. The deep-variational autoencoder network is trained using the target frame and its ground truth image. The variational encoder network constructs non-handcrafted feature sets, and the decoder-network transforms the feature sets into the segmented binary map. Moreover, the variational autoencoder with skip architecture accurately segment the moving objects. The skip architecture used to combine the fine and the coarse scale feature information. Experiments conducted on ‘changedetection.net-2014 (CDnet-2014)’ dataset show that the variational autoencoder based algorithm produces significant results when compared with the classical background modeling methods.
Neurocommunicative methodologies: attention and emotion of the audiovisual story in the classroom

&lt;p class="Textoindependiente21"&gt;Audiovisual content as a learning tool has been incorporated extensively into lecture classes. Emotion-cognition is intrinsic to the functioning of the human brain, and therefore can explain the acquisition of knowledge and competencies in the educational field, and more specifically, the transformative impact on digital natives. Advances in the study of the brain have allowed for quantitative measurement of attentional (EDL) and emotional (EDR) terms. The objective of this article is to analyze and evaluate the correlation between attention and emotion during the viewing of two videos shown in a classroom in the academic space of a university. The method consisted of recording the electrodermal activity of various groups of Audiovisual Communication and Advertising and Public Relations students during the viewing of the two audiovisual stories. The main results and conclusions link characteristics of the audiovisual narrative and technical-expressive qualities and objectives of the videos with the levels of EDL and EDR recorded by the device and establish advanced lines of research in the field of neuroeducation and neurocommunication.&lt;/p&gt;
A Brief Survey of Algorithms, Architectures, and Challenges toward Extreme-scale Graph Analytics

The notion of networks is inherent in the structure, function and behavior of the natural and engineered world that surround us. Consequently, graph models and methods have assumed a prominent role to play in this modern era of Big Data, and are taking a center stage in the discovery pipelines of various data-driven scientific domains. In this paper, we present a brief review of the state-of-the-art in parallel graph analytics, particularly focusing on iterative graph algorithms and their implementation on modern day multicore/manycore architectures. The class of iterative graph algorithms covers a broad class of graph operations of varying complexities, from simpler routines such as Breadth-First Search (BFS), to polynomially-solvable problems such as shortest path computations, to NP-Hard problems such as community detection and graph coloring. We cover a set of common algorithmic abstractions used in implementing such iterative graph algorithms, state the challenges around parallelization on contemporary parallel platforms (including commodity multicores and emerging manycore platforms), and describe a set of approaches that have led to efficient implementations. We also report on advances in manycore architectural frameworks that have found application in parallel graph analytics. We conclude the paper identifying potential research directions, opportunities, and challenges that lay ahead in the path toward enabling graph analytics at exascale.
ADABA: An Algorithm to Improve the Parallel Search in Competitive Agents

Distributed approaches of the Alpha-Beta search algorithm are commonly used as decision-making resources in agents designed for operating in competitive environments involving elevated state spaces. In this context, the present paper proposes a new master-slave based distributed version of Alpha-Beta, named ADABA (Asynchronous Distributed Alpha-Beta Algorithm), which is inspired upon the relevant Alpha-Beta distributed version known as APHID (Asynchronous Parallel Hierarchical Iterative Deepening). ADABA aims at enhancing the APHID’s performance in the following ways: establishing a new policy for ordering the tasks that are executed by the slaves; and including a thread pool dedicated to improving the operation of the master processor in regards to the results returned by the slave processors. ADABA’s performance is validated by means of tournaments involving two Checkers agents trained by Reinforcement Learning: the ADABA-based Checkers player named ADABA-Draughts, implemented in this paper, and the well successful APHID-based agent called APHID-Draughts.
Evaluation of unsupervised optical flow methods for deep learning in real world datasets

The creation of large labeled datasets for optical ow is often infeasible due to the difficulty associated with measuring dynamic objects in real scenes. Current datasets from real world scenes are often sparse in terms of ground truth. Generating synthetic datasets where ground truth can be easily obtained tends to be the easiest way to acquire the large labeled datasets required to achieve good performance. Often, the switch from synthetic to real world imagery leads to a drop-in performance. Recently with the development of differentiable image warping layers, unsupervised methods, which require no ground truth optical ow, can be applied to train a deep neural network (DNN) model for optical ow tasks, and this allows for training with un-labeled video. Brightness constancy assumption is the underlying principle that enables unsupervised learning of optical ow. Violations of the brightness constancy assumption of optical ow in particular at occlusions results in large outlier errors which are harmful to the learning process. The use of robust regression loss function and outlier prediction methods attempt to alleviate the problem of outliers. In this paper, we will conduct experiments to compare performance various unsupervised optical ow methods by exploring the performance of different robust cost functions, and outlier methods.
Acquiring Classifiers for Bipolarized Reward by XCS in a Continuous Reward Environment

In data mining, it is important to clarify how effective the acquired rules are and which elements are affected by rule evaluation. Extended learning classifier system (XCS) reveals factors that affect the classifier (rule) evaluation by generalizing the multiple classifiers that acquire the same reward (evaluation value) into a generalized classifier. In a real-world problem, because the reward of the classifier varies, XCS cannot acquire the generalized classifier. As useful classifiers with a narrow range of the acquired rewards are required, this paper proposes a new XCS (XCS based on reward bipolarization: XCS-RB) that acquires the classifiers that acquire only high rewards and classifiers that acquire only low rewards. XCS-RB was applied to the problems such as predicting the ratio of the deep sleep time of the night of the day from the care plan implemented in the care house and predicting the calculation time of a matrix-matrix product using SGEMM GPU kernel. XCS-RB acquired rules indicating care plan that leads to deep sleep and parameter settings with short calculation time. XCS-RB was able to acquire the generalized classifiers so as not to conflict with the input data; in this paper, the potential advantages of XCS-RB have been demonstrated.
Phonological typology in Optimality Theory and Formal Language Theory: goals and future directions

Much recent work has studied phonological typology in terms of Formal Language Theory (e.g. the Chomsky hierarchy). This paper considers whether Optimality Theory grammars might be constrained to generate only regular languages, and also whether the tools of formal language theory might be used for constructing phonological theories similar to those within Optimality Theory. It offers reasons to be optimistic about the first possibility, and sceptical about the second.
Adaptive markov inference game optimization (AMIGO) for rapid Discovery of satellite behaviors

Space superiority requires space protection and space situational awareness (SSA), which rely on rapid and accurate space object behavioral and operational intent discovery. The presence of adversaries in addition to real-time and hidden information constraints greatly complicates the decision-making process in controlling both ground-based and spacebased surveillance assets. This paper develops and implements a solution called Adaptive Markov Inference Game Optimization (AMIGO) for rapid discovery of satellite behaviors. AMIGO is an adaptive feedback game theoretic approach. AMIGO gets information from sensors about the relations between the resident space objects (RSOs) of interest and ground and space surveillance assets (GSAs). The relations are determined by both the RSOs and GSAs. Therefore, AMIGO represents the situation as a game instead of a control problem. The game reasoning utilizes data level fusion, stochastic modeling/propagation, and RSO detection/tracking to predict the future RSOs-GSAs relations. The game engine also supports optional space pattern dictionary/semantic rules for adaptive transition matrices in the Markov game. If no existing pattern dictionary is available, AMIGO builds an initial one and revises it during the game reasoning. The outputs of the AMIGO reasoning include two kinds of control methods: processing of GSA measurements and localization of RSOs. The two sets form a game equilibrium, one for surveillance asset management and the other for the estimation of RSO behaviors. Numerical simulations and visualizations demonstrate the performance of AMIGO.
On the KDM-CCA Security from Partial Trapdoor One-Way Family in the Random Oracle Model

Abstract In PKC 2000, Pointcheval presented a generic technique to make a highly secure cryptosystem from any partially trapdoor one-way function in the random oracle model. More precisely, any suitable problem providing a one-way cryptosystem can be efficiently derived into a chosen-ciphertext attack (CCA) secure public key encryption (PKE) scheme. In fact, the overhead only consists of two hashing and a XOR. In this paper, we consider the key-dependent message (KDM) security of the Pointcheval’s transformation. Unfortunately, we do not know how to directly prove its KDM-CCA security because there are some details in the proof that we can not bypass. However, a slight modification of the original transformation (we call twisted Pointcheval’s scheme) makes it possible to obtain the KDM-CCA security. As a result, we prove that the twisted Pointcheval’s scheme achieves the KDM-CCA security without introducing any new assumption. That is, we can construct a KDM-CCA secure PKE scheme from partial trapdoor one-way injective family in the random oracle model.
Fast Algorithm for Prototypes Selection—Trust-Margin Prototypes

The k nearest neighbour method (kNN) can be used not only on an entire data set, but also after a selection of instances is performed. Selection of instances should select prototypes which well represent the knowledge about a given problem. We propose a new algorithm of prototype selection. The algorithm is based on selection of instances which represent the borders between classes and additionally they are trustworthy instances. Moreover, our algorithm was optimized with a forest of dedicated locality sensitive hashing (LSH) trees to speed up the prototype selection and the classification process. The algorithm’s final expected complexity is $$O(m\log m)$$ . Additionally, results show that the new algorithm lays ground for accurate classification.
Generating large scale images using GANs

Generative Adversarial Networks (GANs) have been used for the task of image generation and has achieved impressive results. There is always a challenge to train networks that generate large scale images since they tend to be huge and training needs a lot of data. In this work, we tackle this problem by dividing it into two smaller parts. We first generate small scale images using GANs then use a super resolution network to enlarge the generated images resulting in large scale images. Using a super resolution network helps in adding more details to the image which results in a better-quality image. This technique has been tested with a small amount of data to generate 128x128 pixel images and obtained better inception scores over the baseline GAN.
Creating Learning Opportunities On-the-Go

This chapter addresses the question: How could mobile devices help students develop deep authentic engagement with professional knowledge work and the capacity to be practitioners and learners on-the-go? It specifically focuses on how mobile technologies could be used to engage students in active (co-)construction of shared professional knowledge. First, this chapter discusses the relationship between mobile work, mobile learning and knowledge creation for, and in, practice. Then, it discusses how students’ authentic development of various professional knowledge artefacts, such as digital professional learning resources or shareable representations of their professional practice, can help students learn from the specificity of a given work environment and contribute to the learning of others. After, the chapter discusses what it means to learn on-the-go and offers a set of pedagogical strategies to support the development of students’ capacities for learning on-the-go throughout their professional career. These pedagogical strategies engage students in (co-)creation of digital professional knowledge artefacts.