Reviewerly/exHarmony huggingface dataset:
Evaluating the Students’ Attitude Toward the Use of Moodle Mobile Application at Zarqa University in the COVID-19 Pandemic

This study aimed to evaluating the students' attitude toward the use of Moodle mobile application at Zarqa University (ZU) in the covid-19 pandemic. The study used the descriptive analytical approach, and the study sample consisted of (368) students who were randomly selected of undergraduate studies in ZU, and to achieve the objectives of the study, the researchers designed a questionnaire as a tool for collecting data from the members of the study sample. Among undergraduate students in Jordanian universities, There are statistically significant differences in the use of the mobile phone "Moodle mobile application" in distance learning for bachelor's degree students at private Jordanian universities at the significance level (=0.05). This research focuses on the elements that influence students' adoption of the Moodle mobile application and its ability to be used effectively. As a conceptual research framework of Moodle mobile application adoption, the technology acceptance model (TAM) is used. This paper aims to evaluate factors affecting the student's attitude toward the use of the Moodle mobile application at Zarqa University (ZU) in Jordan. The following recommendations were made based on the findings of the study, which showed that students are interested in and satisfied with the Moodle mobile application: Encouraging faculty members and undergraduate students in Jordanian universities to use Moodle mobile application in teaching, design of educational technology lessons for undergraduate and postgraduate subjects to used it in teaching through the Moodle mobile application in order to achieve the goals of distance teaching.
The Role of Information Technology to Fight the Covid-19 Pandemic (Jordan is a Model)

Jordan, like other countries, was affected by the global pandemic (COVID-19) which required the need for researchers' efforts to develop the latest electronic devices, modern technologies, and digital applications to limit the spread and mitigate the damage caused by it. Information systems and technology played a greater role in this global pandemic. In Jordan, researchers developed various technology innovations and applications to fight the COVID-19 pandemic. This paper studies the information technology investments to respond to this pandemic. It also presents a questioner about information technology investments from this research, the results conclude the strength and the ability of information technology have an important role to cope with damage from COVID-19.
Wi-ID: WiFi-Based Identification System Using Rock-Paper-Scissors Hand Gestures

This paper proposes using a WiFi-based identification system, Wi-ID, to identify users from their unique hand gestures. Hand gestures from the popular game rock-paper-scissors are utilized for the system’s user authentication commands. The whole feature of three hand gestures is extracted instead of the single gesture feature extracted by the existing methods. Dynamic time warping (DTW) is utilized to analyze the amplitude information in the time domain based on linear discriminant analysis (LDA), while extract amplitude kurtosis (AP-KU) and shape skewness (SP-SK) are utilized to analyze the Wi-Fi signals energy distribution in the frequency domain. Based on the contributions of the extracted features, the random forests algorithm is utilized for weight inputs in the long short-term memory (LSTM) network. The experiment is conducted on a computer installed with an Intel 5300 wireless networking card to evaluate the effectiveness and robustness of the Wi-ID system. The experiment results showed the accuracy of the proposed Wi-ID system has a personal differentiation accuracy rate over 92%, and with an average accuracy of 96%. Authorized persons who performed incomplete hand gestures are identified with an accuracy of 92% and hostile intruders can be identified with a probability of 90%. Such performance demonstrates that the Wi-ID system achieved the aim of user authentication.
Forgery detection in medical images with distinguished recognition of original and tampered regions using density-based clustering technique

Telemedicine is one of the most significant part of the medical field. It enables transfer of medical images over the internet which offers telediagnosis facility. Due to transfer of medical images from different channels, detection of their authenticity is a crucial research concern. Copy-move forgery is one of the most popular technique for image manipulation. We propose a method for copy-move forgery detection in medical images. Our method apply boundary extraction followed by Laplacian blob detection from the image to identify regions with similar properties. Further, we utilize Good Features To Track (GFTT) and BinBoost techniques for keypoint extraction and descriptor computation, respectively. Similar descriptors are identified using Hamming distance-based nearest neighbor search. Clustering over keypoints is performed using Ant Colony Density-based Clustering (ACDC) technique. We employ Fast Sample Consensus (FSC) technique for selection of correct matches and removal of imprecise keypoints. Further, we use correlation map generation for localization of manipulated regions. Experimental outcomes display that our technique can efficaciously identify tampered medical images even when manipulated images are sustaining various geometrical and post-processing attacks. Proposed technique can also efficaciously distinguish between original and tampered region within forged medical images using Haralick texture features.
VCFL: A verifiable and collusion attack resistant privacy preserving framework for cross-silo federated learning

To prevent privacy information leakage through model parameters in federated learning, many works use homomorphic encryption to protect clients’ updates. However, most of them result in significant computation and communication overhead. Even worse, few of them have considered the correctness of the aggregated results and collusion attack between internal curious clients and the server. In this paper, we propose VCFL, an efficient verifiable and collusion attack resistant privacy preserving framework for cross-silo federated learning. Firstly, we design a homomorphic signcryption mechanism to sign and encrypt model parameters in one go. Secondly, we employ the blinding technique to resist collusion attack between clients and the server. Moreover, we leverage the batching approach to further reduce its computation and communication overhead. Finally, we simulate VCFL in FedML on real world datasets and models. Extensive experimental results show that VCFL can guarantee model performance while protecting privacy, and it is more efficient in both computation and communication than similar frameworks.
Few-shot human motion prediction using deformable spatio-temporal CNN with parameter generation

Human motion prediction is to forecast future pose sequence based on the observed pose sequence. Most recent works rely on large datasets for training. However, the model trained by large datasets cannot be directly applied to the few-shot motion prediction task, which requires the model to generalize well on novel motion dynamics with limited training samples. To deal with this issue, we propose a Motion Prediction Network (MoPredNet) for few shot human motion prediction, which elegantly models long term dependency in motion dynamics and can adapt to predict new forms of motion dynamics. Specifically, the MoPredNet dynamically captures the most informative poses in the data stream as masked poses and adaptively learns spatio-temporal structure from the past poses and the masked poses, and thus improves its encoding capability of motion dynamics. We also propose to cluster training samples into pseudo actions to accumulate prior knowledge, and use the accumulated prior knowledge and few training samples to adapt the MoPredNet to unseen motion dynamics. Experimental results demonstrate that our method achieves better performance over state-of-the-art methods in human motion prediction.
Origin and re-Os geochronology of vein bitumen in the Nanpanjiang basin, SW China: Implication for the ore-forming age of Carlin-type gold deposits

The Nanpanjiang basin has the second-largest concentration of Carlin-type gold deposits in the world. Although significant progress has been made during the past two decades, the age of gold mineralization has not been well constrained. Field observation shows that these gold deposits are spatially associated with hydrocarbon reservoirs. At the Lannigou gold deposit, solid bitumens exist as films within structural fractures and cut through the orebodies, indicating that these bitumens formed after gold mineralization. In this work, we systematically studied bitumen reflectance (Rb), trace element, carbon isotope, and Re-Os isotopic compositions. These results show that these bitumens have high Rb values ranging from 2.40 to 2.71, indicating that they are high-maturity pyrobitumen. Trace element analysis reveals that bitumens in the deposit have high V/Ni, V/(V + Ni), and Zr/Sc ratios, but low Ni/Co, La/Sc ratios, and exhibit relatively flat rare-earth element (REE) patterns with no obvious Ce or Eu anomalies, comparable to the corresponding elemental ratios and REE patterns of Permian source rocks in the region. These bitumens also have similar δ13C values (-24.4 ‰ to –23.8 ‰, mean = -24.1 ‰) to the Permian source rocks (–22.4 ‰ to 24.7 ‰, mean = –23.6 ‰), indicating that they originate from the Permian source rocks. Five bitumen samples cut through the orebody yield a well-defined isochron age of 173 ± 6 Ma, indicating that the Lannigou gold deposit formed before this time. Our new data, combined with previously published geochronological data, suggest that the majority of Carlin-type gold deposits in the Nanpanjiang basin were mineralized at late Triassic to early Jurassic. The Re-Os data of this study also imply that the bitumen Re-Os chronometer has a large potential to constrain the timing of metal mineralization in low-temperature hydrothermal deposits.
Playful punctuation in primary children’s narrative writing

Framed within the fields of applied linguistics and language play, the writing of three attainment groups of 9–11-year-old children was used to investigate their use of ludic (playful) punctuation in the composition of an imaginative narrative. The scripts were from a larger repeat-design study of writing development that addressed a range of linguistic features. It became apparent that the scripts contained a range of language play features, including the use of ludic punctuation, and these were then systematically studied. A total of 71 stories were analysed to identify and categorise the playful punctuation that the children used in this writing task. A range of ludic punctuation was found, most notably in the use of quotation marks, exclamation marks and question marks. In contrast, the asterisk, interrobang, underlining, brackets and dash were used infrequently. There were clear differences between the attainment groups in the content and range of these uses. The findings indicate that 10–11 year-old children find that narrative writing offers a range of opportunities for creative and playful uses of punctuation, even though they were not prompted to do this. The findings also suggest that such uses – like language play as a whole – warrant further investigation.
Robot Obstacle Avoidance Controller Based on Deep Reinforcement Learning

As the core technology in the field of mobile robots, the development of robot obstacle avoidance technology substantially enhances the running stability of robots. Built on path planning or guidance, most existing obstacle avoidance methods underperform with low efficiency in complicated and unpredictable environments. In this paper, we propose an obstacle avoidance method with a hierarchical controller based on deep reinforcement learning, which can realize more efficient adaptive obstacle avoidance without path planning. The controller, with multiple neural networks, contains an action selector and an action runner consisting of two neural network strategies and two single actions. Action selectors and each neural network strategy are separately trained in a simulation environment before being deployed on a robot. We validated the method on wheeled robots. More than 200 tests yield a success rate of up to 90%.
Analysis of Practical Training Characteristics and Teaching System Reform Path of College Physical Education Curriculum Based on Deep Learning

With the growth of the new international alignment, the establishment of multifunctional and applied universities and the training of high-quality talents have become the key tasks of all colleges and universities. With the encouragement of national policies, college physical education has gradually become one of the key subjects for students. As a novel way of pursuing learning goals, deep learning includes high-level and high-stage cognitive management strength or innovative thinking strength. With the background about the widely respected teaching about school sports education of deep learning mode, this article puts forward the analysis of practical training characteristics and educational system reform path of college physical education based on deep learning. The results of the experiment are as follows: (1) this article discusses principles and ideas of the immediate in-depth learning mode and the vacancies in the current college physical education curriculum practical teaching system, determines the research direction of the experiment, and analyzes the characteristics of school physical education practical training as well as the innovation path of the teaching system based on the in-depth learning mode, which provides a technical guarantee for the research of this article; (2) using deep learning algorithm, neural network decomposition method, and recurrent neural network algorithm, the investigation content is identified, analyzed, and calculated through experiments, which is not enough to effectively and accurately analyze the root cause of the problem; it also optimizes and improves according to specific problems, reducing unnecessary research work and time consumption.
Design and Development of IoT Wearable Device for Early Detection of COVID-19 and Monitoring Through Efficient Data Management Framework in Pre-pandemic Life

COVID-19 virus named CORONA is a vigorous disease spread all over the world very quickly and creates a pandemic situation to the human beings normal life. As per the doctors and researchers from the laboratory point of view, it will spread to a huge volume when humans are not followed certain principles. Moreover, this disease is easily transferred to neighbors and others in a short period which leads to death. To rectify the remedy for this virus, various spread countries and research peoples are creating the vaccines and some precautionary methods for living hood situation. Recent techniques are used to detect and monitoring the COVID-19–affected person’s lifestyle and insisting they take precaution steps for early pre-pandemic life. IoT is a framework that is used to generate data from the human body from the sensors opted for human conditions. Wearable devices have been created with these sensors and communicated with human bodies directly or indirectly. The generated data will send through the server using any connectivity techniques such as Bluetooth or Wi-Fi. Analytics will be done at the server side for taking actions like the human body is affected by the COVID-19 virus or not. Finally, the generated data from a human can continuously store in real time in a cloud server which will be managed as a framework efficiently. This research work proposes a framework for data management in the early detection and monitoring of COVID-19 persons through IoT wearable devices in a pre-pandemic life. The experiments have been done at different zones, and the results are shown symptoms of COVID-19 disease. Parallel work reveals the data management in a cloud server since data have generated continuously in real time and tracking details also stored genuinely. Data management is the typical process in this research because all the data were generated in real time and analytics will be done whenever required. For that large amount of space and effective retrieval technique is required for data extraction. This research work data set is derived from various Internet sources like government web sites and mobile applications, and then, results have displayed the COVID-19 disease details accurately in real time.
Collaborative learning network for head pose estimation

Head pose estimation is an important task in many real-world applications, such as human–computer interaction, driver monitoring, face localization and gaze estimation. In this paper, we present a novel collaborative learning framework based on Convolutional Neural Networks (CNNs) for head pose estimation. The proposed framework consists of a landmark-based branch and a landmark-free branch. The former first estimates facial landmarks and then follows the Landmark-MLP-Mixer module which models the complex nonlinear mapping relationship from facial landmarks to head pose angles. While the later adopts a label distribution learning strategy to estimate head pose. The two branches both dedicate themselves to head pose estimation task, and they collaborate with each other for mutual promotion and complementary semantic learning. Specifically, we introduce a dual-branch transfer module in the middle of the network to achieve explicit semantic interaction and introduce a multi-loss strategy that induces to implicit information interaction. We conduct extensive experiments on several popular benchmarks, including AFLW, AFLW2000 and BIWI, the results show that our method is competitive compared to other state-of-the-art methods.
Digital Image Watermarking Techniques Using Machine Learning—A Comprehensive Survey

Digital image watermarking is the most interesting and active field for research as it prevents unwanted access to multimedia data. The trade-off between imperceptibility, robustness, capacity and safety must be maintained for the conception of an efficient and strong digital picture watermarking system. Different studies have been conducted in order to ensure that these needs are hybridized by many domains, including spatial and transformational fields. An analytical analysis is performed on existing digital picture watermarking systems in this research. The digital information that has resulted in the request for a safe ownership of the information may recently be readily changed, reproduced, distributed and stored. The watermark solution for the authentication of content and copyright protection is quite good. This paper discusses basic concepts and features of digital watermarking, important attacks on watermarking systems, general embedding and extraction processes for watermarking marks, and important techniques for the transformation using machine learning are analysed. The objective of this paper is to provide an ephemeral study and background on the definition, and idea and major contributions of watermarking the techniques are classified according to different categories: host signal, sensitivity, robustness, kind of watermark, essential data for extraction, processing domain and applications.
Boosting Code Search with Structural Code Annotation

Code search is a process that takes a given query as input and retrieves relevant code snippets from a code base. The relationship between query and code is commonly built on code annotation, which is extracted from code comments or other documents. The current code search studies approximately treat code annotation as a common natural language, regardless of its hidden structural information. To address the information loss, this work proposes a code annotation model to extract features from five perspectives, and further conduct a code search engine, i.e., CodeHunter. CodeHunter is evaluated on a dataset of 7 million code snippets and query descriptions. The experimental results show that CodeHunter obtains more effective results than Lucene and DeepCS. And we also prove that the effectiveness comes from the rich features and search models, CodeHunter can work well with different sizes of query descriptions.
Content-Based Image Retrieval Using Multi-deep Learning Models

CBIR—content-based image retrieval is commonly known as the process or technique to the “image retrieval” problem that is the problem of analyzing and searching for a real content of images. Image search is a search technique that uses images as a source to retrieve an image that is similar to the given image. This technique has many applications in various fields and industries: securities, banking, education, business, etc. For the past decade, a variety of approaches has been introduced to solve image search problem, and one of the approaches which proves to deliver the highest results is the deep learning CNN model. This paper presents the approach of using multi-deep learning algorithms and similarity measurement. The problem is solved with three pre-trained CNN deep learning models: RestNet50, RestNet101, and VGG19 to extract features, then based on these features to calculate the cosine similarity between images to find the mostly similar images with the given query image. We obtain some encouraging results from several experiments on flower dataset. The results show that the CNN method has succeeded in supporting the retrieval task and therefore has huge potential for practical applications.
Ensembling Sparse Autoencoders for Network Covert Channel Detection in IoT Ecosystems

Network covert channels are becoming exploited by a wide-range of threats to avoid detection. Such offensive schemes are expected to be also used against IoT deployments, for instance to exfiltrate data or to covertly orchestrate botnets composed of simple devices. Therefore, we illustrate a solution based on Deep Learning for the detection of covert channels targeting the TTL field of IPv4 datagrams. To this aim, we take advantage of an Autoencoder ensemble to reveal anomalous traffic behaviors. An experimentation on realistic traffic traces demonstrates the effectiveness of our approach.
MoDMaCAO: a model-driven framework for the design, validation and configuration management of cloud applications based on OCCI

To tackle the cloud-provider lock-in, the open grid forum is developing the open cloud computing interface (OCCI), a standardized interface for managing any kind of cloud resources. Besides the OCCI Core model, which defines the basic modeling elements for cloud resources, further standardized extensions exist that reflect the requirements of different cloud service levels, such as infrastructure and platform elements. However, so far the OCCI platform extension is very coarse-grained and lacks supporting use cases and implementations. Especially, it does not define how the components of the application itself can be managed. In this paper, we discuss the features of MoDMaCAO, a model-driven framework that extends the OCCI platform extension. The users of the framework are able to design and validate cloud application topologies and subsequently deploy them on OCCI compliant clouds by using configuration management tools.