Reviewerly/exHarmony huggingface dataset:
Semi-supervised classification on graphs using explicit diffusion dynamics

Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples. Intuitively, the graph acts as a conduit to channel and bias the inference of class labels. Here, we study classification methods that consider the graph as the originator of an explicit graph diffusion. We show that appending graph diffusion to feature-based learning as an a posteriori refinement achieves state-of-the-art classification accuracy. This method, which we call Graph Diffusion Reclassification (GDR), uses overshooting events of a diffusive graph dynamics to reclassify individual nodes. The method uses intrinsic measures of node influence, which are distinct for each node, and allows the evaluation of the relationship and importance of features and graph for classification. We also present diff-GCN, a simple extension of Graph Convolutional Neural Network (GCN) architectures that leverages explicit diffusion dynamics, and allows the natural use of directed graphs. To showcase our methods, we use benchmark datasets of documents with associated citation data.
An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms

The development of automated image registration (IR) methods is a well-known issue within the computer vision (CV) field and it has been largely addressed from multiple viewpoints. IR has been applied to a high number of real-world scenarios ranging from remote sensing to medical imaging, artificial vision, and computer-aided design. In the last two decades, there has been an outstanding interest in the application of new optimization approaches for dealing with the main drawbacks present in the early IR methods, e.g., the Iterative Closest Point (ICP) algorithm. In particular, nature-inspired computation, e.g., evolutionary computation (EC), provides computational models that have their origin in evolution theories of nature. Moreover, other general purpose algorithms known as metaheuristics are also considered in this category of methods. Both nature-inspired and metaheuristic algorithms have been extensively adopted for tackling the IR problem, thus becoming a reliable alternative for optimization purposes. In this contribution, we aim to perform a comprehensive overview of the last decade (2009–2019) regarding the successful usage of this family of optimization approaches when facing the IR problem. Specifically, twenty-four methods (around 16 percent) of more than one hundred and fifty different contributions in the state-of-the-art have been selected. Several enhancements have been accordingly provided based on the promising outcomes shown by specific algorithmic designs. Finally, our research has shown that the field of nature-inspired and metaheuristic algorithms has increased its interest in the last decade to address the IR problem, and it has been highlighted that there is still room for improvement.
The effect of tacit knowledge and organizational learning on financial performance in service industry

Knowledge is considered the most crucial component for an organization to attain competitiveness.A strong emphasis on explicit knowledge rather than on tacit knowledge (TK) has been observed in information and communication technologies.A change has placed TK in the forefront of the study of organizational learning (OL) and financial performance (FP).These establishments innovate through the transformation of learning, while creating appropriate methods that respond to shifting environments.On the other hand, FP is the target of any establishment; growth and profitability are measured as essential factors for a firm's existence and survival.Several studies have already highlighted the importance of TK, but the impact of TK remains unclear.Moreover, there is no such study on developing countries, such as Saudi Arabia, that are striving to transform the economic structure into a knowledge-based economy.To fill these gaps, this study explores how TK and OL enhance FP.A sample of 168 questionnaires from Saudi's service sector was analyzed using AMOS 26 to test the relationships among TK, OL, and FP using structural equation modelling (SEM).Also, confirmatory factor analysis (CFA) was used to confirm the validity of the measurement model, showing a reliable fit for the data collected in Saudi Arabia's service sector.The SEM results confirmed that TK and OL are crucial elements of knowledge management (KM).TK and OL also generate new systems of continual change, leading to maximized organizational FP.This study develops TK as a quantifiable measure and connects this to organizational performance.The conclusion offers suggestions for management, limitations, and procedures for future research in this area.
Hybrid linear amplifier-involved detection for continuous variable quantum key distribution with thermal states*

Continuous-variable quantum key distribution (CVQKD) can be integrated with thermal states for short-distance wireless quantum communications. However, its performance is usually restricted with the practical thermal noise. We propose a method to improve the security threshold of thermal-state (TS) CVQKD by employing a heralded hybrid linear amplifier (HLA) at the receiver. We find the effect of thermal noise on the HLA-involved scheme in near-and-mid infrared band or terahertz band for direct and reverse reconciliation. Numerical simulations show that the HLA-involved scheme can compensate for the detriment of thermal noise and hence increase the security threshold of TS-CVQKD. In near-and-mid infrared band, security threshold can be extended by 2.1 dB in channel loss for direct reconciliation and 1.6 dB for reverse reconciliation, whereas in terahertz band, security threshold can be slightly enhanced for the gain parameter less than 1 due to the rise in thermal noise.
Brain MR Image Classification Based on Deep Features by Using Extreme Learning Machines

Luigi Barberini1,2*, Francesco Marrosu3, Antonella Muroni3, Elisa Scapin2, Antonella Balestrieri2, Jasjit S Suri4 and Luca Saba1,2 Author Affiliations 1Department of Medical Sciences and Public Health, University of Cagliari, Italy 2Department of Medical Imaging, University of Cagliari, Italy 3Department of Medical Sciences and Public Health, Section of Neurology, University of Cagliari, Italy 4Stroke Monitoring and Diagnostic Division, USA Received: February 21, 2020 | Published: February 27, 2020 Corresponding author: Luigi Barberini, Department of Medical Sciences and Public Health and Medical Imaging, University of Cagliari, Italy DOI: 10.26717/BJSTR.2020.25.004201
The Evolution of Artificial Intelligence and the Possibility of its Application in Cyber Games

Artificial intelligence, as a separate field of research, is currently experiencing a boom - new methods of machine learning and hardware are emerging and improving, and the results achieved change the life of society. Machine translation, handwriting recognition, speech recognition are changing our reality. The work of creating unmanned vehicles, voice assistants and other devices using these technologies is in an active process. The article examines the historical context of the artificial intelligence development, it evaluates the possibilities of its introduction into cyber games, as a safe and effective platform for testing new methods of machine learning. The promotion of such projects can increase the reputation of development companies, ensure increased user confidence in other products and, with a competent marketing strategy, cause a significant public resonance among video game fans, providing the developer with economic profit.
Improving Object Detection from Scratch via Gated Feature Reuse.

In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i.e., without pre-trained models). We call our module GFR (Gated Feature Reuse), which exhibits two main advantages. First, we introduce a novel gate-controlled prediction strategy enabled by Squeeze-and-Excitation to adaptively enhance or attenuate supervision at different scales based on the input object size. As a result, our model is more effective in detecting diverse sizes of objects. Second, we propose a feature-pyramids structure to squeeze rich spatial and semantic features into a single prediction layer, which strengthens feature representation and reduces the number of parameters to learn. We apply the proposed structure on DSOD and SSD detection frameworks, and evaluate the performance on PASCAL VOC 2007, 2012 and COCO datasets. With fewer model parameters, GFR-DSOD outperforms the baseline DSOD by 1.4%, 1.1%, 1.7% and 0.6%, respectively. GFR-SSD also outperforms the original SSD and SSD with dense prediction by 3.6% and 2.8% on VOC 2007 dataset. Code is available at: this https URL .
Analysis of Deep Feature Loss Based Enhancement for Speaker Verification

Data augmentation is conventionally used to inject robustness in Speaker Verification systems. Several recently organized challenges focus on handling novel acoustic environments. Deep learning based speech enhancement is a modern solution for this. Recently, a study proposed to optimize the enhancement network in the activation space of a pre-trained auxiliary network. This methodology, called deep feature loss, greatly improved over the state-of-the-art conventional x-vector based system on a children speech dataset called BabyTrain. This work analyzes various facets of that approach and asks few novel questions in that context. We first search for optimal number of auxiliary network activations, training data, and enhancement feature dimension. Experiments reveal the importance of Signal-to-Noise Ratio filtering that we employ to create a large, clean, and naturalistic corpus for enhancement network training. To counter the mismatch problem in enhancement, we find enhancing front-end (x-vector network) data helpful while harmful for the back-end (Probabilistic Linear Discriminant Analysis (PLDA)). Importantly, we find enhanced signals contain complementary information to original. Established by combining them in front-end, this gives ~40% relative improvement over the baseline. We also do an ablation study to remove a noise class from x-vector data augmentation and, for such systems, we establish the utility of enhancement regardless of whether it has seen that noise class itself during training. Finally, we design several dereverberation schemes to conclude ineffectiveness of deep feature loss enhancement scheme for this task.
Some Criteria of the Knowledge Representation Method for an Intelligent Problem Solver in STEM Education

Nowadays, building intelligent systems for science, technology, engineering, and math (STEM) education is necessary to support the studying of learners. Intelligent problem solver (IPS) is a system that can be able to solve or tutor how to solve the problems automatically. Learners only declare hypothesis and goal of problems based on a sufficient specification language. They can request the program to solve it automatically or to give instructions that help them to solve it themselves. Knowledge representation plays a vital role in these kinds of intelligent systems. There are various methods for knowledge representation; however, they do not meet the requirements of an IPS in STEM education. In this paper, we propose the criteria of a knowledge model for an IPS in education. These criteria orient to develop a method for knowledge representation to meet actual requirements in practice, especially pedagogical requirements. For proving the effectiveness of these criteria, a knowledge model is also constructed. This model can satisfy these criteria and be applied to build IPS for courses, such as mathematics and physics.
Towards a Task and Resource Aware Task Scheduling in Cloud Computing: An Experimental Comparative Evaluation

Cloud computing platform has attracted users to provide them high processing and huge memory-intensive services in a pay as you go manner.In the case of Cloud computing, the computing and storage resources are provisioned to the Cloud users from a pool of configured resources according to the user requirements [1][2][3].In most of the cases, the efficient mapping of tasks [4] to the Virtual Machines (VMs) lead to the user satisfaction level, minimizing the task execution time, and improvement in terms of energy consumption.The task Scheduling approaches are classified into either static or dynamic type.In the case of static task scheduling, the tasks to VM mapping are planned and the scheduling is performed according to the planned mapping.However, for the dynamic scheduling approaches, the scheduling primarily relies on the run-time dynamics of the scheduled tasks according to the user requirements thus may require VM migration [5][6][7].In the case of the static scheduling, VM migration is avoided and ultimately results in reducing the overhead and task execution time.Moreover, the static task scheduling approaches produce improved turnaround time and optimum Quality of Service (QoS) due to the availability of the computing resources for task execution [1].With all its benefits, the static scheduling can suffer from inefficient and under-utilization of the Cloud resource utilization due to run-time workload and the computing environment [8].In the context of Cloud computing, the scheduling is employed at two levels that is: scheduling of VMs on the Physical hosts and the scheduling of tasks on the available VM resources.To evaluate the performance of the task scheduling heuristics various metrics are considered.These parameters include Makespan, Throughput, Average Response Time (ART), Average Resource Utilization Ratio (ARUR).Many researchers have proposed numerous task scheduling heuristics [9][10][11][12][13][14][15][16][17] keeping in view different objectives.However, the question about selecting an appropriate scheduling approach for a given application (i.e., task computation requirements and the available resources) to accomplish the required scheduling objectives like reduced response time and energy consumption, higher ARUR, etc.) is still complex [4,18,19].As different scheduling heuristics are associated with different underlying assumptions; thus, performing a precise comparison cannot be guaranteed.In this research work, we have considered some renown state-of-theart static task scheduling approaches [i.e., Proactive Simulationbased Scheduling and Load Balancing (PSSLB) [20], Minimum Completion Time (MCT) [13], Max-Avg [12], MinMin [11], Load Balance Improved MinMin (LBIMM) [10], Task-Aware Scheduling Algorithm (TASA) [17], Resource-Aware Scheduling Algorithm (RASA) [15], and Sufferage [16]].
A PointNet-Based Solution for 3D Hand Gesture Recognition

Gesture recognition is an intensively researched area for several reasons. One of the most important reasons is because of this technology's numerous application in various domains (e.g., robotics, games, medicine, automotive, etc.) Additionally, the introduction of three-dimensional (3D) image acquisition techniques (e.g., stereovision, projected-light, time-of-flight, etc.) overcomes the limitations of traditional two-dimensional (2D) approaches. Combined with the larger availability of 3D sensors (e.g., Microsoft Kinect, Intel RealSense, photonic mixer device (PMD), CamCube, etc.), recent interest in this domain has sparked. Moreover, in many computer vision tasks, the traditional statistic top approaches were outperformed by deep neural network-based solutions. In view of these considerations, we proposed a deep neural network solution by employing PointNet architecture for the problem of hand gesture recognition using depth data produced by a time of flight (ToF) sensor. We created a custom hand gesture dataset, then proposed a multistage hand segmentation by designing filtering, clustering, and finding the hand in the volume of interest and hand-forearm segmentation. For comparison purpose, two equivalent datasets were tested: a 3D point cloud dataset and a 2D image dataset, both obtained from the same stream. Besides the advantages of the 3D technology, the accuracy of the 3D method using PointNet is proven to outperform the 2D method in all circumstances, even the 2D method that employs a deep neural network.
Adversarial Risk via Optimal Transport and Optimal Couplings

Modern machine learning algorithms perform poorly on adversarially manipulated data. Adversarial risk quantifies the error of classifiers in adversarial settings; adversarial classifiers minimize adversarial risk. In this paper, we analyze adversarial risk and adversarial classifiers from an optimal transport perspective. We show that the optimal adversarial risk for binary classification with 0–1 loss is determined by an optimal transport cost between the probability distributions of the two classes. We develop optimal transport plans (probabilistic couplings) for univariate distributions such as the normal, the uniform, and the triangular distribution. We also derive optimal adversarial classifiers in these settings. Our analysis leads to algorithm-independent fundamental limits on adversarial risk, which we calculate for several real-world datasets. We extend our results to general loss functions under convexity and smoothness assumptions.
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing

Machine learning algorithms are known to be susceptible to data poisoning attacks, where an adversary manipulates the training data to degrade performance of the resulting classifier. In this work, we present a unifying view of randomized smoothing over arbitrary functions, and we leverage this novel characterization to propose a new strategy for building classifiers that are pointwise-certifiably robust to general data poisoning attacks. As a specific instantiation, we utilize our framework to build linear classifiers that are robust to a strong variant of label flipping, where each test example is targeted independently. In other words, for each test point, our classifier includes a certification that its prediction would be the same had some number of training labels been changed adversarially. Randomized smoothing has previously been used to guarantee---with high probability---test-time robustness to adversarial manipulation of the input to a classifier; we derive a variant which provides a deterministic, analytical bound, sidestepping the probabilistic certificates that traditionally result from the sampling subprocedure. Further, we obtain these certified bounds with minimal additional runtime complexity over standard classification and no assumptions on the train or test distributions. We generalize our results to the multi-class case, providing the first multi-class classification algorithm that is certifiably robust to label-flipping attacks.
Online Learning vs. Extreme Learning in Mining Higher Education under COVID

Mining education, including ESL teaching had to undergo a sudden switch to the distance learning mode in spring 2020 due to COVID-19. The article analyses the challenges that most of teachers had to face and justifies the term “extreme learning” in connection with a sudden shift to a distance mode in higher education world-wide. The analytical data is provided to prove the point and as an effort to evaluate the effectiveness of extreme online learning. Current situation in Russian mining universities can be compared with the results of transition to emergency online format in the United States, UNM University. The necessity for further assessment is underlined in connection with the analysis of prerequisites, needs, and processes rather than the results.
An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

In the recent few years, there was a concentrated search on Arabic Optical Character Recognition (OCR), especially the recognition of scanned, offline, machine-printed documents. However, Arabic OCR consequences are dissatisfying and are still a developed research area. Finding the best feature extraction techniques and selecting an appropriate classification algorithm lead to supreme recognition accuracy and low computational overhead. This paper presents a new Arabic OCR model by integrating both of Genetic Algorithm (GA) and the Fuzzy K-Nearest Neighbor classifier (F-KNN) in a unified framework to enhance the identification accuracy. GA is utilized as a feature selection algorithm that has better convergence and spread of solutions with candid variation preservation mechanism. The F-KNN algorithm is more appropriate to classify ambiguous or uncertain data objects in the sense that every object belongs to all classes with different degrees of membership. The suggested model semantically fuses bio-inspired based feature vectors with fuzzy KNN classifier to build accurate membership function for each class. Experimental results compared to other approaches revealed the effectiveness of the suggested model and demonstrated that the feature selection approach increased the identification accuracy process.
Factors influencing the success of information systems in flood early warning and response systems context

Flash flood is a natural disaster that often occurs after heavy rain, and it is getting more common nowadays. The flood early warning and response system (FEWRS) can be installed to minimize the level of damage and the number of casualties due to flood by providing accurate and reliable flood data. Unfortunately, the existing number of studies detailing on the factors affecting the efficiency of FEWRS in flood disaster is quite limited. The above issue is addressed in the current work, which involves conducting a comprehensive literature review on the factors that drive the effectiveness of information systems (IS) in FEWRS. The current analysis was based on the Wymer and Regan's standards. From the 66 factors identified from the previous studies on IS adoption, the most significant factors affecting the effectiveness of FEWRS are: system quality, information quality, user satisfaction, service quality, use, perceived usefulness, intention to use,net benefits, perceived ease of use,compatibility, user experience, relative advantage, complexity, perceivedrisks, educational quality, and confirmation, these factors can be constructed to the success model to address the effectiveness of FEWRS in disaster management.
The COVID-19 Mental Health Content Moderation Conundrum

At the time of writing (mid-May 2020), mental health charities around the world have experienced an unprecedented surge in demand. At the same time, record-high numbers of people are turning to social media to maintain personal connections due to restrictions on physical movement. But organizations like the mental health charity Mind and even the UK Government have expressed concerns about the possible strain on mental health that may come from spending more time online during COVID-19. These concerns are unsurprising, as debates about the link between heavy social media use and mental illness raged long before the pandemic. But our newly heightened reliance on platforms to replace face-to-face communication has created even more pressure for social media companies to heighten their safety measures and protect their most vulnerable users. To develop and enact these changes, social media companies are reliant on their content moderation workforces, but the COVID-19 pandemic has presented them with two related conundrums: (1) recent changes to content moderation workforces means platforms are likely to be less safe than they were before the pandemic and (2) some of the policies designed to make social media platforms safer for people’s mental health are no longer possible to enforce. This Social Media + Society: 2K essay will address these two challenges in depth.