Reviewerly/exHarmony huggingface dataset:
Twitter Sentiment Analysis using an Ensemble Weighted Majority Vote Classifier

Sentiment analysis extracts the emotions expressed in text and has been employed in many fields including politics, elections, movies, retail businesses and in recent years microblogs to understand, track and control the human sentiments or reactions toward products events or ideas. Nevertheless challenges such as different styles of writing, use of negation and sarcasm, existence of spelling mistakes, invention of new words etc. provide obstacle in the correct classification of sentiments. This paper provides an ensemble of classifiers framework for sentiment analysis. The proposed weighted majority voting ensemble method combines six models including Naïve Bayes, Logistic Regression, Stochastic Gradient Descent, Random Forest, Decision Tree and Support Vector Machine to form a single classifier. Weights of the individual classifiers of the ensemble are chosen as accuracy or Fl-score by optimizing their performance. This approach combines models based on the simple majority voting as opposed to the one based on weighted majority voting. Additionally, a comparison is drawn among these six individual classifiers to evaluate their performance. The proposed ensemble model is tested on some existing sentiment datasets, including SemEval 2017 Task 4A, 4B and 4C. The results demonstrate that the Logistic Regression classifier is optimal as compared to other individual classifiers. Furthermore, the proposed ensemble weighted majority voting classifier with the six individual classifiers performs better compared to the simple majority voting and all independent classifiers.
Multi-Stage Degradation Homogenization for Super-Resolution of Face Images With Extreme Degradations

Face Super-Resolution (FSR) aims to infer High-Resolution (HR) face images from the captured Low-Resolution (LR) face image with the assistance of external information. Existing FSR methods are less effective for the LR face images captured with serious low-quality since the huge imaging/degradation gap caused by the different imaging scenarios (i.e., the complex practical imaging scenario that generates test LR images, the simple manual imaging degradation that generates the training LR images) is not considered in these algorithms. In this paper, we propose an image homogenization strategy via re-expression to solve this problem. In contrast to existing methods, we propose a homogenization projection in LR space and HR space as compensation for the classical LR/HR projection to formulate the FSR in a multi-stage framework. We then develop a re-expression process to bridge the gap between the complex degradation and the simple degradation, which can remove the heterogeneous factors such as serious noise and blur. To further improve the accuracy of the homogenization, we extract the image patch set that is invariant to degradation changes as Robust Neighbor Resources (RNR), with which these two homogenization projections re-express the input LR images and the initial inferred HR images successively. Both quantitative and qualitative results on the public datasets demonstrate the effectiveness of the proposed algorithm against the state-of-the-art methods.
6DIVE: 6 Degrees-of-Freedom Immersive Video Editor

Editing 6DoF videos using standard video editing tools is challenging, especially for non-expert users. There is a large gap between the 2D interface used for traditional video editing and the immersive VR environment used for replay. In this paper, we present 6DIVE, a 6 degrees-of-freedom (DoF) immersive video editor. 6DIVE allows users to edit these 6DoF videos directly in an immersive VR environment. In this work, we explored options for a timeline representation as well as UI placement suitable for immersive video editing. We used our prototypical implementation of an immersive video editor to conduct a user study to analyze the feasibility and usability of immersive 6DoF editing. We compared 6DIVE to a desktop-based implementation of a VR video editor. Our initial results suggest that 6DIVE allows even non-expert users to perform basic editing operations on videos in VR. While we did not find any statistically significant differences for the workload between the VR and the desktop interface, we found a statistically significant difference in user preference, with a preference for the VR interface. We also found higher ratings for the user experience metrics in VR captured by the user experience questionnaire.
Performance of the Supercomputer Fugaku for Breadth-First Search in Graph500 Benchmark

In this paper, we present the performance of the supercomputer Fugaku for breadth-first search (BFS) problem in the Graph500 benchmark, which is known as a ranking benchmark used to evaluate large-scale graph processing performance on supercomputer systems. Fugaku is a huge-scale Japanese exascale supercomputer that consists of 158,976 nodes connected by the Tofu interconnect D (TofuD). We have developed a BFS implementation that can extract the performance of Fugaku. We also optimize the number of processes per node, one-to-one communication, performance power ratio, and process mapping in the six-dimensional mesh/torus topology of TofuD. We evaluate the BFS performance for a large-scale graph consisting of about 2.2 trillion vertices and 35.2 trillion edges using the whole Fugaku system, and achieve 102,956 giga-traversed edges per second (GTEPS), resulting in the first position of Graph500 BFS ranking in November 2020. This performance is 3.3 times higher than that of Fugaku’s previous system, the K computer.
Teacher collaboration and students’ digital competence - evidence from the SELFIE tool

This paper explores the relationship between students’ digital competence acquisition, teaching practices, and teacher professional learning activities. We analysed insights provided by 59,452 teachers through SELFIE, an online self-reflection tool for schools’ digital capacity. Using ordinary least squares regressions with school fixed effects, we focus on students’ digital competence and find that the use of digital technologies in cross-curricular projects is the teaching practice most related to the acquisition of students’ digital competence. On the other hand, we also find that teachers’ participation in teacher networks is highly correlated with the implementation of cross-curricular projects with digital technologies. The results further suggest that the use of digital technologies for teacher collaboration (in professional learning activities and in implementing cross-curricular projects) can have great potential and importance in the digital age, both for teachers and learners.
A Novel Ground Metric for Optimal Transport-Based Chronological Age Estimation

Label distribution learning (LDL) is the state-of-the-art approach to dealing with a number of real-world applications, such as chronological age estimation from a face image, where there is an inherent similarity among adjacent age labels. LDL takes into account the semantic similarity by assigning a label distribution to each instance. The well-known Kullback-Leibler (KL) divergence is the widely used loss function for the LDL framework. However, the KL divergence does not fully and effectively capture the semantic similarity among age labels, thus leading to suboptimal performance. In this article, we propose a novel loss function based on the optimal transport theory for the LDL-based age estimation. A ground metric function plays an important role in the optimal transport formulation. It should be carefully determined based on the underlying geometric structure of the label space of the application in-hand. The label space in the age estimation problem has a specific geometric structure, that is, closer ages have more inherent semantic relationships. Inspired by this, we devise a novel ground metric function, which enables the loss function to increase the influence of highly correlated ages; thus exploiting the semantic similarity among ages more effectively than the existing loss functions. We then use the proposed loss function, namely, γ -Wasserstein loss, for training a deep neural network (DNN). This leads to a notoriously computationally expensive and nonconvex optimization problem. Following the standard methodology, we formulate the optimization function as a convex problem and then use an efficient iterative algorithm to update the parameters of the DNN. Extensive experiments in age estimation on different benchmark datasets validate the effectiveness of the proposed method, which consistently outperforms state-of-the-art approaches.
Fair multi-owner search over encrypted data with forward and backward privacy in cloud-assisted Internet of Things

The trend towards the Internet of Things (IoT) technology promises the growth of home and organizational automation in the world. Due to the large amount of IoT data stored in the semi-trust cloud server, the IoT is vulnerable to cyberattacks. Therefore, the privacy violation of outsourced data is an obstacle to efficient IoT data storage. Searchable encryption is a promising solution for secure storage and selective data retrieval. However, achieving desired privacy, performance, and attributes such as dynamics, access control, scalability, and fair arbitration is still a challenge in searchable encryption schemes. In this paper, we propose a fair and dynamic multi-owner searchable encryption scheme wherein dynamics does not leak the sensitive information. In the proposed scheme, the verifiability and fair arbitration, respectively, prevent the malicious cloud servers and malicious users from having any motivation to disrupt the accuracy of the exchanged data. Generating a single trapdoor corresponding to the conjunctive keyword query, a client can search over all documents stored in the cloud, the owner of which has allowed the client to access. We also prove that our scheme is secure against the keyword guessing attack in the standard model. Finally, evaluating the performance of the proposed scheme on a real dataset, we demonstrate its efficiency as well.
Pothole and Object Detection for an Autonomous Vehicle Using YOLO

Object Detection is an key software and a fundamental task for an autonomous driving system that provides remarkable change in computer vision. İn recent years, company's are planning to launch autonomous vehicle in an full swing that's the most important ascpets for object detection and one of most challenging task for locating specific object from from multiple objects in a specific scenario. The computer vision and machine learning algorithm is the important tool for detecting objects in and around the environment. In this paper, which consists of two parts The first part is implemented on object detection in the surrounding with Yolo (You Only Look Once)Algorithm provides exact classification and position which is configured on newly created datasets for classes of object: a car, a person, a truck, a bus, traffic light, motorcycle, pothole, wetland uses the Convolutional Neural Network and max-polling layer for prediction that improves detecting of small target and these deep learning technique provides a high accuracy for detecting real world. Detecting potholes in Indian road help the autonomous vehicle to move smoothly without getting struck in the potholes. In part two of the proposed method is implemented on Raspberry pi4 a popular embedded computer board explores suitability for the running objects. That solves the real world problems and improves the impact on detecting objects. Knowing pothole and wetland detection for self-driving vehicle is needed badly to solve the road lay problems like: accident, slowing down the transport system these are solved by deep learning.
Towards a Probabilistic Based Autonomous UAV Mission Planning for Planetary Exploration

The level of autonomy on Unmanned Aerial Vehicles (UAV) to conduct different missions has remarkably increased over the past ten years. However, a higher level of autonomy is needed when UAVs need to deal with uncertainty derived from lack of Global Position Systems, complex exploration missions in remote partially observable environments and sensor failures. Probabilistic techniques such as the Partially Observable Markov Decision Process (POMDP) have been used to deal with uncertainty in the environment and observations from onboard and payload sensors in robotic applications. In this work, we approach the problem of UAV autonomous mission planning and execution for planetary exploration from the perspective and advantages that probabilistic based planning techniques can bring to a remote exploration mission using UAVs. The POMDP mission planning formulation was implemented and tested in a simulated environment with exploration targets and terrain. Furthermore, future and current work to improve the simulation environment and the autonomous mission planning capabilities for planetary exploration using UAVs is discussed.
Sentiment Analysis of Persian Instagram Post: a Multimodal Deep Learning Approach

Instagram is a popular social media that has a wide range of active users from ordinary people to artists and official users. Instagram posts are widely used by users to share text, image or video. Many users use text to describe or complement the images they share. To analyze the sentiment of such posts, both the content of the text and the image should be considered at the same time. This requires modelling of the relationship between the text and image modalities. To address this problem, we propose a multimodal deep learning method. The proposed method utilizes a bi-directional gated recurrent unit (bi-GRU) for processing text comments and a 2-dimensional convolutional neural network (2CNN) for analyzing images. In order to assess the performance of the proposed model, we introduce a new dataset of Instagram posts, MPerInst, containing 512 pairs of images and their corresponding comments written in the Persian language. Implementation results shows that employing both text and image modalities improves polarity detection accuracy and F1-scrore by 23% and 0.24 compared to using only image and text modalities, respectively. Moreover, the proposed model outperforms 11 similar deep fusion models by 11% and 0.1 in terms of accuracy and F1-score. Both the dataset and the codes of our proposed model are publicly available for probable future use.
Analysis on energy efficient green cloud computing

Abstract Cloud computing offers subscription based storage and resources. Cloud computing provides various services to cloud users. Increased use of information and communication technologies (ICT) has led to increase energy cost as well also increased emission of greenhouse gases like CO2. Since energy is an important asset, green cloud computing comes into picture. Green cloud computing can be achieved by applying various approaches, which uses lesser power as well as emits less CO2 gas, which is hazardous to environment. Energy efficient technologies results in decreased overall energy consumption. There is a need of data centre in almost every field like web applications and businesses which results in producing huge amount of CO2 in environment. To make data centres environmental friendly we need energy efficient approaches which will reduce energy consumption and there hazardous effects on environment. Data is being generated at very high speed so the need of data centres will also increase. Increased data centres will then consume more amount of energy. The main objective of these paper is to reviews various approaches applied by researchers to make energy efficient cloud computing.
Deep Learning Algorithms for Secure Robot Face Recognition in Cloud Environments

To improve human-machine interaction in real-time robotic applications, especially in face recognition task, speed is limited because of robot on board limitations. The speed can be improved by incorporating the cloud technology, it supports flexibility in throughput, less cost, usability enhancement and fewer training instruction, high speed, but private cloud has security issues. So in this paper an attempt is made to improve speed and accuracy of recognition in encrypted domain without loosing privacy of robot. An overview of security algorithms like Cryptography based and image processing based schemes are presented. Some experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images (ORL database) and experimental results shows improvement in speed and accuracy of recognition.
Analyzing and Visualizing Deep Neural Networks for Speech Recognition with Saliency-Adjusted Neuron Activation Profiles

Deep Learning-based Automatic Speech Recognition (ASR) models are very successful, but hard to interpret. To gain a better understanding of how Artificial Neural Networks (ANNs) accomplish their tasks, several introspection methods have been proposed. However, established introspection techniques are mostly designed for computer vision tasks and rely on the data being visually interpretable, which limits their usefulness for understanding speech recognition models. To overcome this limitation, we developed a novel neuroscience-inspired technique for visualizing and understanding ANNs, called Saliency-Adjusted Neuron Activation Profiles (SNAPs). SNAPs are a flexible framework to analyze and visualize Deep Neural Networks that does not depend on visually interpretable data. In this work, we demonstrate how to utilize SNAPs for understanding fully-convolutional ASR models. This includes visualizing acoustic concepts learned by the model and the comparative analysis of their representations in the model layers.
Research of the digital competence of teachers in the conditions of digitalization &#x0D; of the educational environment of the school

The authors describe the significance of the problem of forming digital competence of future teachers. In the period of rapid technological development of a digital society and the introduction of digital technologies, the creation of a digital educational environment, there is a need to resolve the contradiction between the demand for teachers with developed digital competence and their insufficient training in the aspect of the digital component for the successful use of digital technologies in professional pedagogical activity. The formation of digital competence is carried out in the process of implementing educational programs of higher education, as well as advanced training and professional training programs. The authors of the article aim to identify the current state of digital competence of a teacher based on the analysis of empirical data obtained during a survey of current teachers and future teachers students of the pedagogical direction on the use of digital technologies in the educational process. Based on the analysis of the list of relevant digital competencies in the field of education, the authors develop the questionnaire questions. The authors analyze the results of the questionnaire and conclude that the level of digital competence of teachers, the degree of readiness and desire of teachers to use digital tools and services in the educational process suggest the need to develop issues of organizing effective online communication of participants in the educational process, productive feedback with students by means of digital technologies; forming a critical assessment of the behavior of students and adjusting their actions when working in a digital educational environment, control over the independence of students in completing educational tasks; applying the results of student digital footprint analysis to eliminate and correct educational deficits of individual students; using the potential of digital tools and services in organizing group work and project activities of students, communication and involvement of students, and others. The authors propose pedagogical tools for the formation of digital competence of a teacher.
Underwater human detection using faster R-CNN with data augmentation

According to WHO, Drowning is a very much serious and every year 3,74,000 people claiming the lives as a public health threat. Underwater human body detection through live video is a very challenging task because of movement of water currents continuously and changes the intensity of water. Detecting people from an underwater video is a very complex and challenging problem as the video can be affected by various factors such as undesired artifacts (e.g. noise), monitoring limitation of cameras, illumination variation etc. It is very important to build the appropriate model to address this problem. The major goal of this paper is to examine the possibility of detect humans in an underwater Ambient using one of the Faster R-Convolution neural network (Faster R-CNN) and data enhancement algorithms and acutely, recall and precise evaluation of the proposed model results.
Caspar: Towards decision making helpers agents for IoT, based on natural language and first order logic reasoning

In the last decade, the market of Internet of Things has become quite disruptive, together with commercial clouds providing connection between every sort of devices and the global network, supported by vocal assistants. On the other hands, such commercial products are limited to work on limited domains, although easily scalable, without aspiring to higher level of reasoning in the field of Decisions Making. In this work, we show a way towards the design of an architecture for building cognitive agents leveraging Natural Language Processing. Such agents will be not based on clouds and do not require any semantic training, plus they will be able of deduction on facts and rules in First Order Logic inferred directly from Natural Language. After the description of the architecture and its underlying components, a case-study is provided to show the effectiveness in cases of direct commands and routines, subordinated also by a Meta-Reasoning in a conceptual space, parsing the utterances with promising real-time performances.
Consensus‐based unscented Kalman filtering over sensor networks with communication protocols

Abstract This article is concerned with the consensus‐based distributed filtering problem for a class of general nonlinear systems over sensor networks with communication protocols. In order to avoid data collisions, the stochastic protocol and the Round‐Robin protocol are respectively introduced to schedule the data transmission between each node and its neighboring ones. A consensus‐based unscented Kalman filtering (UKF) algorithm is developed for the purpose of estimating the system states over sensor networks subject to communication protocols. Moreover, the exponential boundedness of estimation error in mean square is proved for the proposed algorithm. Finally, compared with the extended Kalman filtering, an experimental simulation example is provided to validate the effectiveness of the consensus‐based UKF algorithm.