Reviewerly/exHarmony huggingface dataset:
From Automatic Keyword Detection to Ontology-Based Topic Modeling

In this paper, we propose a novel, two-staged system, for keyword detection and ontology-driven topic modeling. The first stage specializes in keyword detection in which we introduce a novel graph-based unsupervised approach called Collective Connectivity-Aware Node Weight (CoCoNoW) for detecting keywords from the scientific literature. CoCoNoW builds a connectivity aware graph from a given publication text and eventually assigns weight to the extracted keywords to sort them in order of relevance. The second stage specializes in topic modeling, where a domain ontology serves as an attention-map/context for topic modeling based on the detected keywords. The use of an ontology makes this approach independent of domain and language. CoCoNoW is extensively evaluated on three publicly available datasets Hulth2003, NLM500 and SemEval2010. Analysis of results reveals that CoCoNoW consistently outperforms the state-of-the-art approaches on the respective datasets.
Improved Dynamic Window Approach with Ellipse Equations for Autonomous Navigation of Unmanned Surface Vehicle

Autonomous navigation technology for Unmanned Surface Vehicles (USVs) has seen many recent advancements. In order for a USV to operate autonomously, the path to move from its current location to its destination must be planned, and local path planning must be applied for tracking the path and avoiding obstacles. The Dynamic Window Approach (DWA) is a well-known navigation algorithm used for local path planning. The DWA algorithm derives the linear velocity and angular velocity by evaluating the object function defined as destination direction, velocity, and distance from the obstacle. However, because DWA does not consider the velocity and direction of obstacles, USVs may be in danger when avoiding high-speed obstacles. In this paper, we propose an improved DWA algorithm that considers the velocity and direction of obstacles. Whereas the existing DWA algorithm defines the obstacle recognition area as a circle, the proposed DWA algorithm defines the obstacle recognition area as an ellipse and reflects the speed of the obstacle in the ellipse. In this study, we implemented a simulation to compare the existing DWA algorithm with the improved DWA algorithm proposed here. The results confirm that the proposed DWA algorithm can avoid dynamic obstacles more safely than the existing DWA algorithm.
Discriminative repair approach to remove shadow-induced error for typical digital fringe projection

In a typical digital fringe projection (DFP) system, the shadows in the fringe images cause errors in the phase map. We propose a novel discriminative repair approach to remove the shadow-induced error in the phase map. The proposed approach first classifies the shadow area in the phase map obtained by the DFP into two categories: valid shadow area and invalid shadow area. Then the valid shadow area is repaired by a proposed neighboring information fusion phase estimation (NIFPE) method, which fuses the phase gradient into the result of kernel density estimation (KDE) through the Kalman filter (KF) algorithm. The invalid shadow area is repaired by a proposed background phase matching (BPM) method. The experimental results demonstrate that the shadow-induced error in the phase map can be removed, which verifies the effectiveness of the proposed approach.
Regression Tree Based Explanation for Anomaly Detection Algorithm

This work presents EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces), a novel approach to address explanation using an anomaly detection algorithm, ADMNC, which provides accurate detections on mixed numerical and categorical input spaces. Our improved algorithm leverages the formulation of the ADMNC model to offer pre-hoc explainability based on CART (Classification and Regression Trees). The explanation is presented as a segmentation of the input data into homogeneous groups that can be described with a few variables, offering supervisors novel information for justifications. To prove scalability and interpretability, we list experimental results on real-world large datasets focusing on network intrusion detection domain.
Generic gripper for an unmanned aerial vehicle

Research has been performed on the use of a robotic hand to be used as a UAV gripper and the contributions it would make to implement such a product with a UAV. An overview was performed on UAV grippers available for purchase to determine what contributions would be of greatest need from a newly developed UAV gripper. It was found that there is a lack of grippers capable of gripping objects of a variety of shapes. Design features of the mechanical system and control system of the robotic hand that provides functionality of a UAV gripper which is designed to grip objects of various sizes was then discussed. The robotic hand was tested on performing grips on objects of various shapes and sizes to determine its versatility as a gripper and was shown to be used on all the objects tested. Grip tests performed by the robotic hand to evaluate its functionality showed that the robotic hand was capable of lifting 30.4 N if an object is hooked onto it.
Large deviations of generalized renewal process

Abstract Let ( ξ ( i ), η ( i )) ∈ ℝ d +1 , 1 ≤ i &lt; ∞, be independent identically distributed random vectors, η ( i ) be nonnegative random variables, the vector ( ξ (1), η (1)) satisfy the Cramer condition. On the base of renewal process, N T = max{ k : η (1) + … + η ( k ) ≤ T } we define the generalized renewal process Z T = $\begin{array}{} \sum_{i=1}^{N_T} \end{array}$ ξ ( i ). Put I Δ T ( x ) = { y ∈ ℝ d : x j ≤ y j &lt; x j + Δ T , j = 1, …, d }. We find asymptotic formulas for the probabilities P ( Z T ∈ I Δ T ( x )) as Δ T → 0 and P ( Z T = x ) in non-lattice and arithmetic cases, respectively, in a wide range of x values, including normal, moderate, and large deviations. The analogous results were obtained for a process with delay in which the distribution of ( ξ (1), η (1)) differs from the distribution on the other steps. Using these results, we prove local limit theorems for processes with regeneration and for additive functionals of finite Markov chains, including normal, moderate, and large deviations.
Learning object-centric complementary features for zero-shot learning

Zero-shot learning (ZSL) aims to recognize new objects that have never seen before by associating categories with their semantic knowledge. Existing works mainly focus on learning better visual-semantic mapping to align the visual and semantic space, while the effectiveness of learning discriminative visual features is neglected. In this paper, we propose an object-centric complementary features (OCF) learning model to take full advantage of visual information of objects with the guidance of semantic knowledge. This model can automatically discover the object region and obtain fine-scale samples without any human annotation. Then, the attention mechanism is used in our model to capture long-range visual features corresponding to semantic knowledge like ‘four legs’ and subtle visual differences between similar categories. Finally, we train our model with the guidance of semantic knowledge in an end-to-end manner. Our method is evaluated on three widely used ZSL datasets, CUB, AwA2, and FLO, and the experiment results demonstrate the efficacy of the object-centric complementary features, and our proposed method outperforms the state-of-the-art methods.
Discursos de instituições de saúde brasileiras sobre atividade física no início da pandemia de COVID-19

The aim of this article is to analyze discourses produced by health institutions regarding physical activity in the beginning of the COVID-19 pandemic (March and early April) in Brazil. A documentary study was carried out using websites of health institutions. The process were theoretical-methodological support inspired in Foucault’s studies based on the intertwining notion of discourse and its impact on the conduct of the government. A total of 17 documents were compiled, three from government institutions, nine from medical societies and five from physical education institutions. The first documents addressed the personal and equipment hygiene concerning closed places, the following communications were about restricting physical activities in these places, finally recommending the performance of these activities at home. Therefore, physical activity predominated in its biological perspective, especially affecting immune function. The problematization of the discourses was carried out based on the governmentality notion in which individuals and families were triggered to practice physical activities at home without instrumentalization, access to knowledge and to physical education professionals.
Retailing and the Ethical Challenges and Dilemmas Behind Artificial Intelligence

Learning Outcomes Learn about ethical dilemmas in retailing with the advances in artificial intelligence (AI) Be able to reflect critically about the use and potential misuse of AI within the retailing industry Learn about the history of AI Be able to define and classify AI
GENERATIVE ADVERSARIAL NETWORKS AS A NOVEL APPROACH FOR TECTONIC FAULT AND FRACTURE EXTRACTION IN HIGH-RESOLUTION SATELLITE AND AIRBORNE OPTICAL IMAGES

Abstract. We develop a novel method based on Deep Convolutional Networks (DCN) to automate the identification and mapping of fracture and fault traces in optical images. The method employs two DCNs in a two players game: a first network, called Generator, learns to segment images to make them resembling the ground truth; a second network, called Discriminator, measures the differences between the ground truth image and each segmented image and sends its score feedback to the Generator; based on these scores, the Generator improves its segmentation progressively. As we condition both networks to the ground truth images, the method is called Conditional Generative Adversarial Network (CGAN). We propose a new loss function for both the Generator and the Discriminator networks, to improve their accuracy. Using two criteria and a manually annotated optical image, we compare the generalization performance of the proposed method to that of a classical DCN architecture, U-net. The comparison demonstrates the suitability of the proposed CGAN architecture. Further work is however needed to improve its efficiency.
Multi-Scale Residual Fusion Network for Super-Resolution Reconstruction of Single Image

Inferring all missing high-frequency details by using low-resolution image is the key to super-resolution reconstruction of a single image. In order to extract the feature information in low-resolution images fully and maximize the deduction of high-frequency details, we introduce a multi-scale cross merge (MSCM) network based on residual fusion. The MSCM uses feature extraction module with different-sized convolutional kernels to extract multiple features from the low resolution input image and send them into a nonlinear mapping module after concatenate them together. The nonlinear mapping module consists of five cross-merge modules, each of them formed by cascading three residual dual-branch merged structures. This structure can promote information integration of different branches. Dense connection and residual connection are integrated into the nonlinear mapping module to improve the transmission of information and gradient. The nonlinear mapping module is responsible for extracting high-frequency features and sending them to reconstruction module, which combines an improved sub-pixel up-sampling layer with external residual and global residual to generate a high resolution image. Simulation experiments demonstrate that our MSCM network has the ability of achieving single-image super-resolution reconstruction, and offers objective and subjective quality improvement compared to mainstream methods and other state-of-the-art reconstruction methods.
From Undirected Structures to Directed Graphical Lasso Fuzzy Cognitive Maps using Ranking-based Approaches

Fuzzy cognitive maps (FCMs) have gained popularity within the scientific community due to their capabilities in modelling and decision making for complex problems. However, learning FCM models automatically from data without any expert knowledge and/or historical data remains a considerable challenge. For our research, we use the estimated weight matrix from the graphical lasso (glasso) method with the EBIC regulation technique. Particularly, the glasso is a technique originated from machine learning which is used to model a problem by learning the weight matrix directly from a dataset. Moreover, the relationships are expressed by conditional independence among two nodes after conditioning on all the other nodes of the graph. However, the challenging task in this study is the investigation of the suitable transformation of the weight matrix from a symmetric matrix to asymmetric in order to determine the directions of the edges among the concepts and construct the glassoFCM model. For this reason, statistical comparisons are applied to examine if there are significant differences in the value of the output concept when the input concepts are rearranged according to four different cases. The whole approach was implemented in a business intelligence problem of evaluating the willingness of the employees to work in Belgian companies.
Offline Witness Encryption with Semi-adaptive Security

The first construction of Witness Encryption (WE) by Garg et al. (STOC 2013) has led to many exciting avenues of research in the past years. A particularly interesting variant is Offline WE (OWE) by Abusalah et al. (ACNS 2016), as the encryption algorithm uses neither obfuscation nor multilinear maps. Current OWE schemes provide only selective security. That is, the adversary must commit to their challenge messages $$m_{0}$$ and $$m_{1}$$ before seeing the public parameters. We provide a new, generic framework to construct OWE, which achieves adaptive security in the sense that the adversary may choose their challenge messages adaptively. We call this semi-adaptive security, because – as in prior work – the instance of the considered NP language that is used to create the challenge ciphertext must be fixed before the parameters are generated in the security proof. We show that our framework gives the first OWE scheme with constant ciphertext overhead even for messages of polynomially-bounded size. We achieve this by introducing a new variant of puncturable encryption defined by Green and Miers (S&P 2015) and combining it with the iO-based approach of Abusalah et al. Finally, we show that our framework can be easily extended to construct the first Extractable Offline Witness Encryption (EOWE), by using extractability obfuscation of Boyle et al. (TCC 2014) in place of iO, opening up even more possible applications.
A New Evaluation Method for Medical Image Information Hiding Techniques

Medical image scans and associated electronic medical records (EMR) could be stored locally or transmitted for use in autodiagnosis and remote healthcare in teleradiology. Hence, they require security against unauthorised access and modification. Among other means of providing this security, information hiding (IH) techniques have gained relevance especially for open networks that are prone to active attacks. However, the evaluation of the suitability of these IH algorithms in terms of preserving medical image diagnostic features is currently limited to signal processing parameters. This paper re-interprets existing evaluation parameters and provides a new framework that allows dynamic selection of medical image IH (watermarking and steganography) security algorithms. Specifically, criteria that capture medical statistics used in the diagnosis and monitoring of patients were incorporated. These criteria and framework were validated on the Pneumonia Chest Xray dataset (used in a Kaggle Competition) using three selected IH algorithms that offer privacy and image tamper detection.
DeepFacePencil: Creating Face Images from Freehand Sketches

In this paper, we explore the task of generating photo-realistic face images from hand-drawn sketches. Existing image-to-image translation methods require a large-scale dataset of paired sketches and images for supervision. They typically utilize synthesized edge maps of face images as training data. However, these synthesized edge maps strictly align with the edges of the corresponding face images, which limit their generalization ability to real hand-drawn sketches with vast stroke diversity. To address this problem, we propose DeepFacePencil, an effective tool that is able to generate photo-realistic face images from hand-drawn sketches, based on a novel dual generator image translation network during training. A novel spatial attention pooling (SAP) is designed to adaptively handle stroke distortions which are spatially varying to support various stroke styles and different level of details. We conduct extensive experiments and the results demonstrate the superiority of our model over existing methods on both image quality and model generalization to hand-drawn sketches.
Architecture of A Novel Low-Cost Hardware Neural Network

Hardware-based machine learning is becoming increasingly popular due to its high speed of computation. One of the desired characteristics of such hardware is reduced hardware and design costs. This paper proposes a design approach for a neural network to reduce the cost of hardware in terms of adders and multipliers. Adders and multipliers are parts of the main components in the neural network, and they are used in each node in the network. The proposed approach reduces the number of multipliers and adders in the network by half, which reduces the cost. The proposed technique is based on sharing multiplier and adder between two hidden layers. The method has been tested and validated using multiple datasets. The accuracy of the proposed approach is similar to the traditional methods in the literature, while the proposed approach utilizes only half the number of multipliers and adders. The proposed design is implemented using VHDL and Altera Arria 10 GX FPGA. The simulation result shows the proposed method retains the performance of the network with a 63% reduction in the hardware design with acceptable accuracy.
Evolving Deep Recurrent Neural Networks Using A New Variable-Length Genetic Algorithm

Deep Recurrent Neural Network (DRNN) is an effective deep learning method with a wide variety of applications. Manually designing the architecture of a DRNN for any specific task requires expert knowledge and the optimal DRNN architecture can vary substantially for different tasks. This paper focuses on developing an algorithm to automatically evolve task-specific DRNN architectures by using a Genetic Algorithm (GA). A variable-length encoding strategy is developed to represent DRNNs of different depths because it is not possible to determine the required depth of a DRNN in advance. Activation functions play an important role in the performance of DRNNs and must be carefully used in these networks. Driven by this understanding, knowledge-driven crossover and mutation operators will be proposed to carefully control the use of activation functions in GA in order for the algorithm to evolve best performing DRNNs. Our algorithm focuses particularly on evolving DRNN architectures that use Long Short Term Memory (LSTM) units. As a leading type of DRNN, LSTM-based DRNN can effectively handle long-term dependencies, achieving cutting-edge performance while processing various sequential data. Three different types of publicly available benchmark datasets for both classification and regression tasks have been considered in our experiments. The obtained results show that the proposed variable-length GA can evolve DRNN architectures that significantly outperform many state-of-the-art systems on most of the datasets.
Artificial neural networks training acceleration through network science strategies

Abstract The development of deep learning has led to a dramatic increase in the number of applications of artificial intelligence. However, the training of deeper neural networks for stable and accurate models translates into artificial neural networks (ANNs) that become unmanageable as the number of features increases. This work extends our earlier study where we explored the acceleration effects obtained by enforcing, in turn, scale freeness, small worldness, and sparsity during the ANN training process. The efficiency of that approach was confirmed by recent studies (conducted independently) where a million-node ANN was trained on non-specialized laptops. Encouraged by those results, our study is now focused on some tunable parameters, to pursue a further acceleration effect. We show that, although optimal parameter tuning is unfeasible, due to the high non-linearity of ANN problems, we can actually come up with a set of useful guidelines that lead to speed-ups in practical cases. We find that significant reductions in execution time can generally be achieved by setting the revised fraction parameter ( $$\zeta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ζ</mml:mi></mml:math> ) to relatively low values.
Statistical Profiles of Users’ Interactions with Videos in Large Repositories: Mining of Khan Academy Repository

The rapid growth of instructional videos repositories and their widespread use as a tool to support education have raised the need of studies to assess the quality of those educational resources and their impact on the quality of learning process that depends on them.Khan Academy (KA) repository is one of the prominent educational videos' repositories.It is famous and widely used by different types of learners, students and teachers.To better understand its characteristics and the impact of such repositories on education, we gathered a huge amount of KA data using its API and different web scraping techniques, then we analyzed them.This paper reports the first quantitative and descriptive analysis of Khan Academy repository (KA repository) of open video lessons.First, we described the structure of repository.Then, we demonstrated some analyses highlighting content-based growth and evolution.Those descriptive analyses spotted the main important findings in KA repository.Finally, we focused on users' interactions with video lessons.Those interactions consisted of questions and answers posted on videos.We developed interaction profiles for those videos based on the number of users' interactions.We conducted regression analysis and statistical tests to mine the relation between those profiles and some quality related proposed metrics.The results of analysis showed that all interaction profiles are highly affected by video length and reuse rate in different subjects.We believe that our study demonstrated in this paper provides valuable information in understanding the logic and the learning mechanism inside learning repositories, which can have major impacts on the education field in general, and particularly on the informal learning process and the instructional design process.This study can be considered as one of the first quantitative studies to shed the light on Khan Academy as an open educational resources (OER) repository.The results presented in this paper are crucial in understanding KA videos repository, its characteristics and its impact on education.