Reviewerly/exHarmony huggingface dataset:
From lab-based studies to eye-tracking in virtual and real worlds: conceptual and methodological problems and solutions.

Video stream: https://vimeo.com/357473408&#x0D; Wearable mobile eye trackers have great potential as they allow the measurement of eye movements during daily activities such as driving, navigating the world and doing groceries. Although mobile eye trackers have been around for some time, developing and operating these eye trackers was generally a highly technical affair. As such, mobile eye-tracking research was not feasible for most labs. Nowadays, many mobile eye trackers are available from eye-tracking manufacturers (e.g. Tobii, Pupil labs, SMI, Ergoneers) and various implementations in virtual/augmented reality have recently been released.The wide availability has caused the number of publications using a mobile eye tracker to increase quickly. Mobile eye tracking is now applied in vision science, educational science, developmental psychology, marketing research (using virtual and real supermarkets), clinical psychology, usability, architecture, medicine, and more. Yet, transitioning from lab-based studies where eye trackers are fixed to the world to studies where eye trackers are fixed to the head presents researchers with a number of problems. These problems range from the conceptual frameworks used in world-fixed and head-fixed eye tracking and how they relate to each other, to the lack of data quality comparisons and field tests of the different mobile eye trackers and how the gaze signal can be classified or mapped to the visual stimulus. Such problems need to be addressed in order to understand how world-fixed and head-fixed eye-tracking research can be compared and to understand the full potential and limits of what mobile eye-tracking can deliver. In this symposium, we bring together presenting researchers from five different institutions (Lund University, Utrecht University, Clemson University, Birkbeck University of London and Rochester Institute of Technology) addressing problems and innovative solutions across the entire breadth of mobile eye-tracking research.&#x0D; Hooge, presenting Hessels et al. paper, focus on the definitions of fixations and saccades held by researchers in the eyemovement field and argue how they need to be clarified in order to allow comparisons between world-fixed and head-fixed eye-tracking research. - Diaz et al. introduce machine-learning techniques for classifying the gaze signal in mobile eye-tracking contexts where head and body are unrestrained. Niehorster et al. compare data quality of mobile eye trackers during natural behavior and discuss the application range of these eye trackers. Duchowski et al. introduce a method for automatically mapping gaze to faces using computer vision techniques. Pelz et al. employ state-of-the-art techniques to map fixations to objects of interest in the scene video and align grasp and eye-movement data in the same reference frame to investigate the guidance of eye movements during manual interaction.
Secure your cloud workloads with IBM Secure Execution for Linux on IBM z15 and LinuxONE III

With the growth of IBM Z and LinuxONE in the cloud, customers are expecting their workloads and data to have the same levels of security, isolation, and privacy as running on-premise. In order to achieve these levels of trust, the IBM z15 and LinuxONE III provide the IBM Secure Execution for Linux facility, which isolates customers’ data from each other, as well as from the cloud administrators. Unlike other solutions in the industry, IBM Secure Execution does not require remote attestation, thus simplifying the deployment of applications into the protected environment. Also, unlike some other solutions in the industry, the integrity of data is protected end-to-end, that is, from the boot image on disk to memory as it is paged by the hypervisor and throughout execution. The isolation and integrity are provided by hardware and trusted firmware known as the ultravisor. In this article, we describe the security model of IBM Secure Execution, the functionality of the hardware and ultravisor, as well as the required changes to the hypervisor in order to support protected virtual machines.
Are Product Owners communicators? A multi‐method research approach to provide a more comprehensive picture of Product Owners in practice

Abstract Product Owners have an important role in the agile and hybrid software development process. While this role is supposed to maximize the value of a product, there seem to be several scattered results on how they achieve this, as well as what actually constitutes this role in practice. To consolidate current research results and to further analyze the key attribute of Product Owners, we conducted a multi‐method research approach spanning a systematic mapping study and a consecutive case study in a hybrid development environment. The results of the mapping study states that Product Owners are communicators. We further investigated on this and used the shadowing technique to observe three Product Owners' communication activities. The results support that statement, as the gained data reveal that Product Owners spend 65% of their time in meetings. But rather than just providing the team with the necessary requirements for the product under development, Product Owners need this time to synchronize and align their work, streamline the agile process of large‐scale Scrum, discuss team‐based topics, and to solve upcoming issues addressed by the team. These results contribute to draw a more comprehensive picture of the important but yet complex role of Product Owners in practice.
Real-time path optimization of mobile robots based on improved genetic algorithm

The emergence of intelligent mobile robots has liberated the human labor to a certain extent, especially their abilities to work in harsh environments in place of humans. For intelligent mobile robots, how to achieve fast path optimization is an important issue. In this article, the model establishment method of environmental information collected by robot sensors and the genetic algorithm for real-time optimization of running paths are briefly introduced first, the crossover, mutation probability, and fitness function are improved based on the shortcomings of the traditional genetic algorithm, and then the simulation analysis of the two algorithms is carried out using matrix laboratory (MATLAB) software. The results show that the improved algorithm obtains a smaller length of optimal path, fewer inflection points, and a smaller turning angle, which also converges faster and has a greater degree of fitness. It takes 0.053 s for the traditional algorithm to calculate the optimal path, while the improved algorithm needs 0.013 s. In summary, the improved genetic algorithm can quickly and efficiently calculate the optimal path, which is suitable for real-time path optimization of mobile robots.
Machine Learning Guidance for Connection Tableaux

Abstract Connection calculi allow for very compact implementations of goal-directed proof search. We give an overview of our work related to connection tableaux calculi: first, we show optimised functional implementations of connection tableaux proof search, including a consistent Skolemisation procedure for machine learning. Then, we show two guidance methods based on machine learning, namely reordering of proof steps with Naive Bayesian probabilities, and expansion of a proof search tree with Monte Carlo Tree Search.
S2RSCS: An Efficient Scientific Submission Recommendation System for Computer Science

With the increasing number of scientific publications as well as conferences and journals, it is often hard for researchers (especially newcomers) to find a suitable venue to present their studies. A submission recommendation system would be hugely helpful to assist authors in deciding where they can submit their work. In this paper, we propose a novel approach for a Scientific Submission Recommendation System for Computer Science (S2RSCS) by using the necessary information from the title, the abstract, and the list of keywords in given paper submission. By using tf-idf, the chi-square statistics, and the one-hot encoding technique, we consider different schemes for feature selection, which can be extracted from the title, the abstract, and keywords, to generate various groups of features. We investigate two machine-learning models, including Logistic Linear Regression (LLR) and Multi-layer Perceptrons (MLP), for constructing an appropriate recommendation engine. The experimental results show that using keywords can help to increase the performance of the recommendation model significantly. Prominently, the proposed methods outperform the previous work [1] for different groups of features in terms of top-3 accuracy. These results can give a promising contribution to the current research of the paper recommendation topic.
Explicit diversification of search results across multiple dimensions for educational search

Abstract Making use of search systems to foster learning is an emerging research trend known as search as learning . Earlier works identified result diversification as a useful technique to support learning‐oriented search, since diversification ensures a comprehensive coverage of various aspects of the queried topic in the result list. Inspired by this finding, first we define a new research problem, multidimensional result diversification, in the context of educational search. We argue that in a search engine for the education domain, it is necessary to diversify results across multiple dimensions, that is, not only for the topical aspects covered by the retrieved documents, but also for other dimensions, such as the type of the document (e.g., text, video, etc.) or its intellectual level (say, for beginners/experts). Second, we propose a framework that extends the probabilistic and supervised diversification methods to take into account the coverage of such multiple dimensions. We demonstrate its effectiveness upon a newly developed test collection based on a real‐life educational search engine. Thorough experiments based on gathered relevance annotations reveal that the proposed framework outperforms the baseline by up to 2.4%. An alternative evaluation utilizing user clicks also yields improvements of up to 2% w.r.t. various metrics.
Real-Time Face Recognition Based on Dlib

Although the face recognition method for in-depth learning has high accuracy, the model is complex and the recognition speed is slow. In order to realize real-time face recognition of students while learning video, a real-time face recognition method based on Dlib is proposed. In this essay, based on the real-time face recognition method, Dlib is used as a face recognition tool, and the Dlib environment is configured. A face detection method based on the Dlib toolkit is designed. That is, using the convolution neural network method (CNN) and the deep residual network (ResNet) for real-time face recognition, and finally using the GPU to accelerate the operation of the face feature extraction network, sharing the CPU load to improve the overall operating efficiency of the system. By numerous repeated experiments and tests, it shows that the accuracy of face recognition is high and has certain application value.
Exploiting and Evaluating Live 360° Low Latency Video Streaming Using CMAF

In this paper, we describe the implemented 360° live video streaming architecture and evaluation setup running in the 5G test network (5GTN), which can be considered beneficial for live educational services and providing a fast situation awareness in medical sector. The concentration is on providing more global HTTP adaptive streaming (HAS) architecture using also CDN components rather than having only a point-to-point solution. With this setup, we evaluate the common media application format (CMAF) usage in HAS context in order to have reliable video transmission method for high bitrate 360° video as well as setting the end-to-end (e2e) latency as low as possible. By this, both uplink and downlink direction are investigated by using wired, wireless, or mobile connection. In addition, the experienced e2e latency is measured. The extensive placement of synchronized network measurement indicators from distinct network nodes help to identify the bottlenecks in mobile video delivery path and verify the CMAF-based usefulness in streaming optimization. 5G key performance indicators delay and throughput are the key parameters in 360° live streaming, which can be useful in forming the overall, live view of an educational surgery or even from an accident scene. In such scenarios, mobile networking can be the easiest and often the only option for outputting the important information to relevant users, such as students or doctors.
An FPGA-Based Simple RGB-HSI Space Conversion Algorithm for Hardware Image Processing

In this paper, a new, low-complexity, easy-to-implement hardware method for color space conversion between the red-green-blue (RGB) and the hue-saturation-intensity (HSI) color spaces called the simple RGB-HSI space conversion (S-SC) algorithm is proposed, which aims to provide more rapid computing due to the need for fewer operations.In the S-SC algorithm, we reconstruct the model of space conversion between the RGB color space and the HSI color space (RGB-HSI) by inverting the conversion from the HSI color space to the RGB color space of the traditional geometric derivation algorithm.As a result, the nonlinear model-realized RGB-HSI color space conversion by the geometric derivation algorithm is transformed into a linear conversion model, which can avoid complicated calculations such as trigonometric and inverse trigonometric functions in the color space conversion process.The model can effectively reduce the computational complexity of the algorithm and facilitate hardware implementation at the same time.To evaluate the performance of the S-SC algorithm, we first compare the S-SC algorithm with the geometric derivation algorithm from the computational complexity perspective.On this basis, we compare the S-SC algorithm with five other RGB-HSI color space conversion algorithms from the perspectives of error and conversion effect.Finally, we use the field programmable gate array (FPGA) hardware platform to analyze and verify the timing sequence and logical resource consumption and verify the effectiveness of the proposed algorithm with experimental results.We show that the S-SC algorithm achieves good performance in terms of conversion accuracy, logical unit resource occupancy, and output timing.
A Sentiment Analysis Model for Terrorist Attacks Reviews on Twitter

Twitter is considered as one of the famous microblogs that attract politicians and individuals to express their views on political, economic and social issues. The phenomenon of terrorist operations is one of the largest security and economic problem facing the world in recent years. Twitter users' comments on terrorism issues are important to understand users' sentiment about terrorist events. Sentiment analysis is a field of research for understanding and extracting users’ views. In this paper, we propose a model for automatically classifying users’ reviews on Twitter after occurrence of a terrorist attack, the model is built using lexicon and machine learning approaches. Lexicon approach is used to create labelled training dataset while machine learning approach was used to build the model. Scores of some domain related words were neutralized to avoid their negative effect. Features were selected based on PoS. Majority voting between NB, SVM and LR machine learning classification algorithms was applied. The performance of classification algorithms was measured using accuracy and F1 scores. The results obtained are compared to identify the best classification algorithm for features selection. Result show that our model achieved 94.8% accuracy with 95.9% F1 score.
RSANet: Deep Recurrent Scale-Aware Network for Crowd Counting

Most recent works have made significant progress in crowd counting by fusing multi-scale features directly with weighted sum or concatenation to handle large scale variation problems. Meanwhile, there is very little attention paid on the prediction of high-resolution density maps and predicted low-resolution density maps lead to inaccurate counting results. In this paper, we present a novel recurrent scale-aware network(RSANet) to generate a high-resolution density map with scale-aware feature fusion approach. Within this network, we introduce a coarse-to-fine scheme restoring the high-resolution feature map from a low-resolution feature map progressively with stacked dilated convolution blocks. Then, we incorporate recurrent modules to capture dynamic scale-aware information and to benefit the restoration of high-resolution feature maps through multi-scale feature fusion to generate a high-resolution density map. We also use a multi-resolution supervision strategy for training to improve the performance of our network. Extensive experiments on three challenging crowd counting datasets demonstrate the effectiveness of the proposed method.
Intuitive 3D Control of a Quadrotor in User Proximity with Pointing Gestures

We present an approach for controlling the position of a quadrotor in 3D space using pointing gestures; the task is difficult because it is in general ambiguous to infer where, along the pointing ray, the robot should go. We propose and validate a pragmatic solution based on a push button acting as a simple additional input device which switches between different virtual workspace surfaces. Results of a study involving ten subjects show that the approach performs well on a challenging 3D piloting task, where it compares favorably with joystick control.
Literature review and analysis on big data stream classification techniques

Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodologies, like deep learning based techniques, Convolutional Neural Network (CNN) based techniques, K-Nearest Neighbor (KNN) based techniques, Neural Network (NN) based techniques, fuzzy based techniques, and Support vector based techniques, and so on. Moreover, a detailed study is made by concerning the parameters, like evaluation metrics, implementation tool, employed framework, datasets utilized, adopted classification methods, and accuracy range obtained by various techniques. Eventually, the research gaps and issues of various big data classification schemes are presented.
Text classification system of academic papers based on hybrid Bert-BiGRU model

The text classification is an important research orientation in the fields of information retrieval and data mining, which has extensive applications in the practical work and scientific research and its research on the algorithm is always a hot topic. At present, the study on the long text like academic texts mainly focuses on abstract extraction. Whereas, due to the complicated content of the text format, there is very little classification research according to the text structure. In this study, the academic texts are classified based on Bidirectional Encoder Representations from Transformers and Bidirectional Gated Recurrent Unit (BERT-BiGRU) model.
Type-1 fuzzy logic algorithm for low cost embedded systems

This paper presents in detail a type-1 fuzzy logic algorithm developed in C language to be used in 8-bit low cost embedded systems, adaptable to different applications. It is also shown a rigorous comparison between the proposed algorithm and type-1 fuzzy conventional algorithm, in which the accuracy, the processing speed and especially the low amount of memory necessary for the execution of the proposed code are highlighted. In order to verify the operation of the proposed work, the algorithm was developed for the PIC18 microcontrollers and the results were obtained by simulations with PROTEUS software and functional comparisons with MATLAB software.
Influence of the sampling density on the noise level in displacement and strain maps obtained by processing periodic patterns

The aim of this study is to investigate the influence of the sampling density of periodic patterns such as checkerboards or 2D grids on the noise level observed in displacement and strain maps extracted from such images. A spectral method named Localized Spectrum Analysis (LSA) is used to process the images. It is shown that this parameter influences this noise level. This influence is quantified, and a power law is proposed to model it, with a value of the power identified with the measurements. It is also confirmed that checkerboards give a lower noise level than 2D grids, with an improvement which is also quantified, and which increases as the sampling density increases.
Exploring Web Service QoS Estimation for Web Service Composition

Web development, machine ubiquity, and the availability of communication networks impacted device design, replacing the idea of an isolated personal computer with one of distributed and connected computers. A web service is a component of software which provides a specific functionality that can be accessed over the Internet. Software development through the assembly of independent services follows the Service-Oriented Computing (SOC) paradigm. One key in the SOC model is that third parties provide resources by presenting only external access interfaces. In this context, the analysis of issues related to the quality of service (QoS) becomes crucial for several development activities related to web services, spanning the discovery of services, their selection, composition and their adaptation in client systems. As far as we know, little has been done in terms of estimation of unknown quality attribute levels when those attributes have high priority in client systems. In this study, a linear regression-based statistical approach is explored to evaluate the relationship between the quality attributes provided by Web services and the metrics related to their interfaces defined in WSDL. This issue is a cornerstone in web service composition for verifying and ascertaining the levels of quality attributes provided by candidate services when QoS data is missing. Finally, we illustrate the approach by performing experiments with public QoS web service datasets and service interface metrics, explore its limitations, and delineate future steps.