Reviewerly/exHarmony huggingface dataset:
A New Action Recognition Framework for Video Highlights Summarization in Sporting Events

To date, machine learning for human action recognition in video has been widely implemented in sports activities. Although some studies have been successful in the past, precision is still the most significant concern. In this study, we present a high-accuracy framework to automatically clip the sports video stream by using a three-level prediction algorithm based on two classical open-source structures, i.e., YOLO-v3 and OpenPose. It is found that by using a modest amount of sports video training data, our methodology can perform sports activity highlights clipping accurately. Comparing with the previous systems, our methodology shows some advantages in accuracy. This study may serve as a new clipping system to extend the potential applications of the video summarization in sports field, as well as facilitates the development of match analysis system.
Positive Turn in Elder-Care Workers’ Views Toward Telecare Robots

Abstract Robots have been slowly but steadily introduced to welfare sectors. Our previous observations based on a large-scale survey study on Finnish elder-care workers in 2016 showed that while robots were perceived to be useful in certain telecare tasks, using robots may also prove to be incompatible with the care workers’ personal values. The current study presents the second wave of the survey data from 2020, with the same respondents (N = 190), and shows how these views have changed for the positive, including higher expectations of telecare robotization and decreased concerns over care robots’ compatibility with personal values. In a longitudinal analysis (Phase 1), the positive change in views toward telecare robots was found to be influenced by the care robots’ higher value compatibility. In an additional cross-sectional analysis (Phase 2), focusing on the factors underlying personal values, care robots’ value compatibility was associated with social norms toward care robots, the threat of technological unemployment, and COVID-19 stress. The significance of social norms in robot acceptance came down to more universal ethical standards of care work rather than shared norms in the workplace. COVID-19 stress did not explain the temporal changes in views about robot use in care but had a role in assessments of the compatibility between personal values and care robot use. In conclusion, for care workers to see potential in care robots, the new technology must support ethical standards of care work, such as respectfulness, compassion, and trustworthiness of the nurse–patient interaction. In robotizing care work, personal values are significant predictors of the task values.
Superrobust Geometric Control of a Superconducting Circuit

Geometric phases accompanying adiabatic quantum evolutions can be used to construct robust quantum control for quantum information processing due to their noise-resilient feature. A significant development along this line is to construct geometric gates using nonadiabatic quantum evolutions to reduce errors due to decoherence. However, it has been shown that nonadiabatic geometric gates are not necessarily more robust than dynamical ones, in contrast to an intuitive expectation. Here we experimentally investigate this issue for the case of nonadiabatic holonomic quantum computation (NHQC) and show that conventional NHQC schemes cannot guarantee the expected robustness due to a cross-coupling to the states outside the computational space. We implement a different set of constraints for gate construction in order to suppress such cross-coupling to achieve an enhanced robustness. Using a superconducting quantum circuit, we demonstrate high-fidelity holonomic gates whose infidelity against quasistatic transverse errors can be suppressed up to the fourth order, instead of the second order in conventional NHQC and dynamical gates. In addition, we explicitly measure the accumulated dynamical phase due to the abovementioned cross-coupling and verify that it is indeed much reduced in our NHQC scheme. We further demonstrate a protocol for constructing two-qubit NHQC gates also with an enhanced robustness.
A decomposition approach for large-scale non-separable optimization problems

Large-scale optimization is the key to many practical decision processes. To deal with the dimensional issue in such problems, many approaches incorporate a divide-and-conquer strategy. Among them, cooperative coevolution approaches have recently gained popularity. Depending on the problem’s structure, the decomposition of any large problem, into a number of smaller sub-problems, may leave some variables common in more than one sub-problem. Such a decomposition may have a negative effect on the quality of the final solution of an optimization problem. In this paper, we have proposed an algorithm that incorporates a novel decomposition method, where the objective of decomposition is to minimize the number of common variables between sub-problems, achieved by exploiting a variable interaction matrix developed from the problem. So the algorithm works as a two-stage approach, where the first stage is the problem decomposition, and the second stage is to find the solutions of the problem. The performance of our proposed algorithm is assessed by solving different sets of large-scale non-separable benchmark functions with up to 2,905 variables. The experimental results provide important insights into the efficiency of the proposed decomposition method, which in turn improves the performance of the optimization process.
The visual environment and attention in decision making.

Visual attention is a fundamental aspect of most everyday decisions, and governments and companies spend vast resources competing for the attention of decision makers. In natural environments, choice options differ on a variety of visual factors, such as salience, position, or surface size. However, most decision theories ignore such visual factors, focusing on cognitive factors such as preferences as determinants of attention. To provide a systematic review of how the visual environment guides attention we meta-analyze 122 effect sizes on eye movements in decision making. A psychometric meta-analysis and Top10 sensitivity analysis show that visual factors play a similar or larger role than cognitive factors in determining attention. The visual factors that most influence attention are positioning information centrally, ρ = .43 (Top10 = .67), increasing the surface size, ρ = .35 (Top10 = .43), reducing the set size of competing information elements, ρ = .24 (Top10 = .24), and increasing visual salience, ρ = .13 (Top10 = .24). Cognitive factors include attending more to preferred choice options and attributes, ρ = .36 (Top10 = .31), effects of task instructions on attention, ρ = .35 (Top10 = .21), and attending more to the ultimately chosen option, ρ = .59 (Top10 = .26). Understanding real-world decision making will require the integration of both visual and cognitive factors in future theories of attention and decision making. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Mediating role of innovative climate among leadership and employee performance in textile exporting firms of Pakistan

This research seeks to examine how transformational leadership and ambidextrous leadership impact employee performance. The study focuses on the mediating role of innovation climate in influencing the relationship both directly and indirectly with employee performance. Previous literature shows that many studies have been conducted to measure the employee’s performance under the leadership. Still, no study has covered the mediating role of innovation climate between leadership and employee performance. The present study aims at filling that gap through the presentation of a statistical model. Respondents in the sample included the managerial staff of textile exporting firms of Pakistan. Multiple linear regression and the PROCESS for mediation model in the SPSS was used to analyze the collected data. The results indicated that the CEO transformational leadership and ambidextrous leadership had a significantly positive influence on employee performance. In addition, innovation climate was found to have a strong mediating effect. The researchers recommended the exploration of the impact of other possible moderating variables in future researches. Recommendations were also made for the top management of textile firms for the consideration of innovation climate along with the improvement of employee performance.
LinkedIn's Audience Engagements API

We present a privacy system that leverages differential privacy to protect LinkedIn members' data while also providing audience engagement insights to enable marketing analytics related applications. We detail the differentially private algorithms and other privacy safeguards used to provide results that can be used with existing real-time data analytics platforms, specifically with the open sourced Pinot system. Our privacy system provides user-level privacy guarantees. As part of our privacy system, we include a budget management service that enforces a strict differential privacy budget on the returned results to the analyst. This budget management service brings together the latest research in differential privacy into a product to maintain utility given a fixed differential privacy budget.
Introducing the PEAT model to frame professional digital competence in teacher education

This paper puts forward a case for using the PEAT model in teacher education, a framework designed to capture the different dimensions of teachers’ professional digital competence (PDC). The model arose from an Erasmus+ funded project exploring digital competence in teacher education. While existing frameworks and conceptualisations of teachers’ digital competence exist, this paper argues that the PEAT model has unique affordances and characteristics. This paper outlines the importance of digital competence before exploring how it is currently conceptualised in teacher education. Following this, some of the current frameworks encapsulating the elements of teachers’ professional digital competence are briefly presented. Finally, the paper presents the PEAT framework and discusses its unique affordances.
Novel Metaheuristic Based on Iterated Constructive Stochastic Heuristic: Dhouib-Matrix-3 (DM3)

This paper presents a new metaheuristic named Dhouib-Matrix-3 (DM3) inspired by our recently developed constructive stochastic heuristic Dhouib-Matrix-TSP2 (DM-TSP2) and characterized by only one parameter: the number of iterations. The proposed metaheuristic DM3 is an iterative algorithm in which every iteration is based on two relay hybridization techniques. At first, the constructive stochastic heuristic DM-TSP2 starts by generating a different initial basic feasible solution and then each solution is intensified by the novel procedure Far-to-Near which exchanges far cities by closer ones using three perturbation techniques: insertion, exchange, and 2-opt. Experimental results carried out on the classical travelling salesman problem using the well-known TSP-LIB benchmark instances demonstrate that our approach DM3 outclasses the simulated annealing algorithm, the genetic algorithm, and the cellular genetic algorithm. Furthermore, the proposed DM3 is statistically concurrent to the hybrid simulated annealing cellular genetic algorithm. Nevertheless, DM3 is easier to implement and needs only one parameter to identify (the maximum number of iterations).
Learning Sparse and Discriminative Multimodal Feature Codes for Finger Recognition

Compared with uni-modal biometrics systems, multimodal biometrics systems using multiple sources of information for establishing an individual’s identity have received considerable attention recently. However, most traditional multimodal biometrics techniques generally extract features from each modality independently, ignoring the implicit associations between different modalities. In addition, most existing work uses hand-crafted descriptors that are difficult to capture the latent semantic structure. This paper proposes to learn the sparse and discriminative multimodal feature codes (SDMFCs) for multimodal finger recognition, which simultaneously takes into account the specific and common information among inter-modality and intra-modality. Specifically, given the multimodal finger images, we first establish the local difference matrix to capture informative texture features in local patches. Then, we aim to jointly learn discriminative and compact binary codes by constraining the observations from multiple modalities. Finally, we develop a novel SDMFC-based multimodal finger recognition framework, which integrates the local histograms of each division block in the learned binary codes together for classification. Experimental results on three commonly used finger databases demonstrate the effectiveness and robustness of the proposed framework in multimodal biometrics tasks.
Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans

Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur’s Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.
A Hybrid Approach of Spotted Hyena Optimization Integrated with Quadratic Approximation for Training Wavelet Neural Network

Spotted hyena optimization (SHO) is one of the newly evolved swarm-based metaheuristic optimization methods based on the social life cycle of hyenas. In recent times SHO is being applied to various engineering applications as well as to solve real-life complications. In this paper, we have hybridized SHO with quadratic approximation operator (QAO), termed as QASHO. The proposed QASHO has been scrutinized to enhance the exploitation ability, aiming to achieve global optimum, as QAO performs better within the local confinement region. Furthermore, the proposed approach shows improved strength in terms of escaping from the local minima trap, as in each iteration we discard some of the worst individuals by some suitable ones. To validate the proficiency of the proposed QASHO approach, 28 standard problems have been preferred in connection with IEEE-CEC-2017. The outcome observed from the suggested method has also equated with contemporary metaheuristic approaches. To prove the statistical significance, a nonparametric test has also been accomplished. Additionally as a real-life application, the suggested approach QASHO has utilized to train wavelet higher-order neural networks (HONN) by choosing datasets from the UCI store. The above correlations reveal that QASHO can deal with complex optimization tasks.
A New Forensic Video Database for Source Smartphone Identification: Description and Analysis

In recent years, the field of digital imaging has made significant progress, so that today every smartphone has a built-in video camera that allows you to record high-quality video for free and without restrictions. On the other hand, rapidly growing internet technology has contributed significantly to the widespread use of digital video via web-based multimedia systems and mobile smartphone applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital videos have become affordable nowadays, security issues have become threatening and spread worldwide. One of the security issues is identifying source cameras on videos. There are some new challenges that should be addressed in this area. One of the new challenges is individual source camera identification (ISCI), which focuses on identifying each device regardless of its model. The first step towards solving the problems is a popular video database recorded by modern smartphone devices, which can also be used for deep learning methods that are growing rapidly in the field of source camera identification. In this paper, a smartphone video database named Qatar University Forensic Video Database (QUFVD) is introduced. The QUFVD includes 6000 videos from 20 modern smartphone representing five brands, each brand has two models, and each model has two identical smartphone devices. This database is suitable for evaluating different techniques such as deep learning methods for video source smartphone identification and verification. To evaluate the QUFVD, a series of experiments to identify source cameras using a deep learning technique are conducted. The results show that improvements are essential for the ISCI scenario on video.
Quantum teleportation with one classical bit

Quantum teleportation allows one to transmit an arbitrary qubit from point A to point B using a pair of (pre-shared) entangled qubits and classical bits of information. The conventional protocol for teleportation uses two bits of classical information and assumes that the sender has access to only one copy of the arbitrary qubit to be sent. Here, we ask whether we can do better than two bits of classical information if the sender has access to multiple copies of the qubit to be teleported. We place no restrictions on the qubit states. Consequently, we propose a modified quantum teleportation protocol that allows Alice to reset the state of the entangled pair to its initial state using only local operations. As a result, the proposed teleportation protocol requires the transmission of only one classical bit with a probability greater than one-half. This has implications for efficient quantum communications and the security of quantum cryptographic protocols based on quantum entanglement.
Generation Smart Education Learning Process of Blockchain-Based in Universities

Smart education in universities can be realized with a comprehensive use of IT infrastructure, various systems that are implemented to run this can take priority of the cloud computing and Internet of Things (IoT) and. Resulting in the need for connectivity into IoT gateways and nodes, as well as implementing an architecture that does not only rely on communication coverage via wireless, but needs to reduce energy consumption in order to save IoT node batteries to maximize performance. The use of such an architecture must prioritize blockchain technology that can provide security, accountability, and data transparency that can be managed by universities. This article discusses the key to early adoption of technology that can develop smart learning with smart learning. After the characteristics of the smart education or university are determined, then the details of the latest communication technologies that are most relevant to the smart education application can be analyzed. In addition, higher education requires learning about the use of blockchain. Therefore, this article will provide useful pointers in planning smart education development, as well as responsible and intelligent developers in the next generation.
Role of twitter user profile features in retweet prediction for big data streams

To study the various factors influencing the process of information sharing on Twitter is a very active research area. This paper aims to explore the impact of numerical features extracted from user profiles in retweet prediction from the real-time raw feed of tweets. The originality of this work comes from the fact that the proposed model is based on simple numerical features with the least computational complexity, which is a scalable solution for big data analysis. This research work proposes three new features from the tweet author profile to capture the unique behavioral pattern of the user, namely “Author total activity”, “Author total activity per year”, and “Author tweets per year”. The features set is tested on a dataset of 100 million random tweets collected through Twitter API. The binary labels regression gave an accuracy of 0.98 for user-profile features and gave an accuracy of 0.99 when combined with tweet content features. The regression analysis to predict the retweet count gave an R-squared value of 0.98 with combined features. The multi-label classification gave an accuracy of 0.9 for combined features and 0.89 for user-profile features. The user profile features performed better than tweet content features and performed even better when combined. This model is suitable for near real-time analysis of live streaming data coming through Twitter API and provides a baseline pattern of user behavior based on numerical features available from user profiles only.
D-CNN: A New model for Generating Image Captions with Text Extraction Using Deep Learning for Visually Challenged Individuals

Automatically describing the information of an image using properly constructed sentences is a tricky task in any language. However, it has the potential to have a significant effect by enabling visually challenged individuals to better understand their surroundings. This paper proposes an image captioning system that generates detailed captions and extracts text from an image, if any, and uses it as a part of the caption to provide a more precise description of the image. To extract the image features, the proposed model uses Convolutional Neural Networks (CNNs) followed by Long Short-Term Memory (LSTM) that generates corresponding sentences based on the learned image features. Further, using the text extraction module, the extracted text (if any) is included in the image description and the captions are presented in audio form. Publicly available benchmark datasets for image captioning like MS COCO, Flickr-8k, Flickr-30k have a variety of images, but they hardly have images that contain textual information. These datasets are not sufficient for the proposed model and this has resulted in the creation of a new image caption dataset that contains images with textual content. With the newly created dataset, comparative analysis of the experimental results is performed on the proposed model and the existing pre-trained model. The obtained experimental results show that the proposed model is equally effective as the existing one in subtitle image captioning models and provides more insights about the image by performing text extraction.