Reviewerly/exHarmony huggingface dataset:
An Enhanced Security Scheme for Protecting Sensitive Documents in a Public Cloud Environment Using a Dual Encryption technique

Users must be online as little as possible to use the file transfer service. Only the file&#x0027;s data&#x2014;in identity-based encryption, the receiver&#x0027;s identification as a public key&#x2014;is accessible to the transmitter and recipient. To receive key pairs, it is not essential to maintain track of cryptographic keys or to contact one another in advance. The Private Key Generator (PKG), which generates private key for each user&#x0027;s in traditional identity-based encryption, focuses on the decoding ability. As a result, the PKG necessitates complete faith in PKG, which is difficult to achieve in many circumstances. Currently, sensitive data safeguards using the user&#x0027;s id and birthdates&#x0027;. However, this study proposes a dual encryption approach that doubles encryption. An opponent easily recognizes these facts. As a result, in a dual encryption method, we employ the Identity Based Dual Encryption Cipher (IBDEC) to protect user information in an income tax document. We use Email or a mobile OTP number to send the secret key. Only people with these two keys have access to the document. Existing methods are insecure compared to the suggested method.
Utilisation of Virtual Assistant and Its Impact on Retail Industry

Virtual assistant is nothing but an independent contractor, who offers administrative services to the clients of a particular organisation while operating outside of the office of the client. Generally, a virtual assistant operates from a home-based office. This virtual assistant application has the ability to access the required planning documents, such as shared calendars. The contemporary retail organisations like e-commerce companies in this competitive global business environment are using virtual assistant to enhance omnichannel experience, 24/7 customer service, order tracking, and product recommendations. Overall, virtual assistant helps the organisations in enhancing social media management activities. This concept of the use of virtual assistant has been significantly emerged after the increase in demands for e-commerce business activities in this decade. Research objectives related to the title of this research are developed and listed. Relevant theories on virtual assistant are applied in the literature review section of this study. The researcher has decided to adopt qualitative research methodology to achieve the objectives of the research. Moreover, the researcher has considered secondary data analysis approach to conduct this research. In terms of findings, it has been identified that virtual assistant has a positive impact on the business operation activities of retail organisations. Authentic secondary sources are considered to collect and analyse the data. Some challenges associated with the utilisation of virtual assistant also have been identified in the findings section. Some valuable recommendations are suggested for the future researchers to overcome those identified associated challenges.
A robust intrinsic feature of images derived from the tensor manifold

As an important feature of images, the structure tensor (ST) provides properties of the local image intensities. However, it is a challenging task to analyse images directly using STs since STs are 2nd-order symmetric positive semi-definite matrices. In this paper, we define a robust intrinsic feature of images using ST. In addition, using our feature, we propose an improved image similarity measure. By converting the ST into a symmetric positive definite (called tensor) matrix, we define our feature using the extended geodesic distance of tensors calculated in a Riemannian manifold. Our feature shows the essential natural properties of ST and images as tensors are analysed on the tensor manifold. Moreover, defined by the global embedded geometry of the structure tensor, our feature provides a stable intrinsic property of images. The experiments show that our feature performs well in representing the essential attributes of images, especially the edges and important structures. It also shows that our image similarity measure can accurately detect similar images or patches.
Hand Gesture Recognition Using Capabilities of Capsule Network and Data Augmentation

Gesture Recognition is a technology that makes devices such as a computer capable of recognizing and responding to different gestures produced by the human body. Nonetheless, several factors such as differences in illumination intensity, the complexity of hand gesture models, and other factors can affect the performance of recognition and classification algorithms. Some advances in deep learning such as CapsNets have been suggested to improve the performance of image recognition systems in this particular field. CapsNets emerged to solve part of the limitations of CNN. For this reason, in this work, CapsNets is proposed to solve the American Sign Language problem very effectively. The obtained results showed that the proposed model with a simple data augmentation process produces an test accuracy of 99.08%.
Bridging Virtual and Reality in Mobile Augmented Reality Applications to Promote Immersive Experience

Immersion is a powerful and important interactive experience. However, little is known about how we can facilitate immersion in Mobile Augmented Reality (MAR) applications. Establishing the relationship between the virtual and the real is considered a promising way to promote immersion. To enhance immersion in MAR, we present BRIDGE, an interaction design model which builds a bridge between virtual and reality through the following three kinds of relationships: The virtual object has a close relationship with the real environment where the user is (contextual relationship) ; the virtual object has the same physical properties as the real world (physical relationship) ; the user imitates real-world interactions by directly interacting with the virtual world with their hands (interactive relationship). To evaluate the effect of the BRIDGE model, we implement it into the application design and conduct a comparative study of 32 users, and explore the immersive user experience of contextual and non-contextual, physical and non-physical, natural-interaction and screen-touch. The quantitative and qualitative results show that virtual objects have a stronger presence and users are more immersed in the environment when there is a contextual and physical relationship and users can interact naturally. This study is the first step to having a better understanding of the characteristics that contribute to an immersive experience and how they affect human perception and the presence of virtual objects. We hope to provide design insights for MAR applications based on these results.
Theory-Practicing in Critical Times: Viv Bozalek in Conversation With Kathrin Thiele, Deirdre M. Donoghue, and Pınar Türer

This article is derived from a webinar series conversation titled, “Post Philosophies and the Doing of Inquiry,” co-hosted by Candace R. Kuby and Viv Bozalek. The webinar sessions ran from August 2020 to September 2021. This webinar series was made possible by a research collaborative partnership between the University of Missouri System in the United States and the University of the Western Cape (or UWC) in Cape Town, South Africa. The session with Kathrin Thiele took place in April 2021 and the following article is the transcribed conversation with Kathrin Thiele, also including contributions by Deirdre Donoghue and Pınar Türer.
TACDFSL: Task Adaptive Cross Domain Few-Shot Learning

Cross Domain Few-Shot Learning (CDFSL) has attracted the attention of many scholars since it is closer to reality. The domain shift between the source domain and the target domain is a crucial problem for CDFSL. The essence of domain shift is the marginal distribution difference between two domains which is implicit and unknown. So the empirical marginal distribution measurement is proposed, that is, WDMDS (Wasserstein Distance for Measuring Domain Shift) and MMDMDS (Maximum Mean Discrepancy for Measuring Domain Shift). Besides this, pre-training a feature extractor and fine-tuning a classifier are used in order to have a good generalization in CDFSL. Since the feature obtained by the feature extractor is high-dimensional and left-biased, the adaptive feature distribution transformation is proposed, to make the feature distribution of each sample be approximately Gaussian distribution. This approximate symmetric distribution improves image classification accuracy by 3% on average. In addition, the applicability of different classifiers for CDFSL is investigated, and the classification model should be selected based on the empirical marginal distribution difference between the two domains. The Task Adaptive Cross Domain Few-Shot Learning (TACDFSL) is proposed based on the above ideas. TACDFSL improves image classification accuracy by 3–9%.
Kernel learning with nonconvex ramp loss

Abstract We study the kernel learning problems with ramp loss, a nonconvex but noise‐resistant loss function. In this work, we justify the validity of ramp loss under the classical kernel learning framework. In particular, we show that the generalization bound for empirical ramp risk minimizer is similar to that of convex surrogate losses, which implies kernel learning with such loss function is not only noise‐resistant but, more importantly, statistically consistent. For adapting to real‐time data streams, we introduce PA‐ramp, a heuristic online algorithm based on the passive‐aggressive framework, to solve this learning problem. Empirically, with fewer support vectors, this algorithm achieves robust empirical performances on tested noisy scenarios.
Performance Impact of Differential Privacy on Federated Learning in Vehicular Networks

This article examines differential privacy on federated learning. While recent studies are actively exploring this topic in conventional network environment, there are few studies that address the topic in vehicular networks. In particular, this research investigates the trade-off between accuracy performance and level of data protection. We have applied a local differential privacy with adaptive clipping method to two different federated learning models running on vehicular networks and conduct experiments under two different mobility scenarios. Results show that privacy-enhanced federate learning models degrade performance by 2.96% - 42.97%, representing that a vehicular network is sensitive to vehicles’ mobilities, learning models, and privacy solutions. With initial results, we plan to bring research questions for open discussion to audience.
New Image Encryption Method using Multiple Chaotic Map Computation and Irregular Diffusion Process

With technological advancements, information transfer has become entirely digital, with images serving as a common source of sensitive data. This information must be securely transferred over the internet. This is where image encryption comes into play, and it has been proven to be a very successful method of data transfer in various sectors. In this paper, an efficient algorithm for image encryption is developed where pixel scrambling plays a key role. The encryption method begins with pixel scrambling, which is accomplished by first developing an algorithm for the chaotic map to generate a sequence key. The encrypted image is obtained by applying the generated sequence key to the original image, which is further encrypted using various mathematical computations, such as row and column-wise bit XOR operations, as detailed in the paper. The technique consists of many levels of encryption for securely transmitting data that is vulnerable to cyberattacks. The experimental results demonstrate that the proposed method is efficient.
Inertial Navigation Compensation with Reinforcement Learning

This paper presents a method for applying Reinforcement Learning (RL) techniques to extend the holdover time of an inertial system in the absence of aiding from a Global Navigation Satellite System (GNSS). Several RL algorithms were evaluated using this method. The performance results, in terms of positional error, for each algorithm are compared to each other as well as to the results from an unaided Kalman Filter and a navigation-grade Inertial Navigation System.
From nostalgia to knowledge: Considering the personal dimensions of data lifecycles

Abstract Data lifecycle models are used to visualize the stages data resources pass through from creation to completion of a research project and beyond. Although helpful, these models tend to depict idealized and impersonal circumstances that often involve curation actions taken by researchers themselves, while neglecting to represent dynamics particular to researchers' lives and careers. Meanwhile, research data are still infrequently managed successfully according to these models, which is a particular concern for “long tail” data, where sharing tends to depend on individual researchers' decisions and preservation actions. The paper addresses these considerations by exploring the ways in which long tail data lifecycles are entwined with human career lifecycles. Through interviews with astronomers, six affective dimensions that may impact data and career lifecycle dynamics are identified: painstakingness, loose ends, altruism, intellectual passion, legacy, and nostalgia. Building upon these insights and holistically drawing connections between human information behavior and data lifecycle modeling literature and frameworks, a researcher‐centered lifecycle model is proposed that seeks to depict the unique motivations and changing needs of researchers to share data throughout the courses of their careers. The paper concludes with suggestions for theoretical development and curatorial interventions in current data lifecycle modeling and data management practice.
Library Personalized Service System Based on Computer Network Technology

In order to solve the library’s demand for computer network technology, a research on the library’s personal system service is proposed. A library-based self-service model was originally designed and developed. The system is developed from six aspects: resource association analysis and mining, reading interest analysis, data collection, personal service, personal service scheduling, and data warehouse. Secondly, it shows that this study has done some research on library application itself and completed some research. Finally, libraries can accomplish personal services in a variety of ways, such as distributing modifications, smart administrators, vertical portals, pushers, and more. In foreign libraries and universities represented by North Carolina State University and the Data and Information Center of the Chinese Academy of Sciences, the construction of personal data in digital libraries has become an important part of future development. According to the law, 57.9% and 65.0% of users ranked search engines as second only to e-mail in the China Internet Improvement Data released by the China Internet Network Information Center in 2000 and 2005, respectively.
Constructing a cross-document event coreference corpus for Dutch

Event coreference resolution is a task in which different text fragments that refer to the same real-world event are automatically linked together. This task can be performed not only within a single document but also across different documents and can serve as a basis for many useful Natural Language Processing applications. Resources for this type of research, however, are extremely limited. We compiled the first large-scale dataset for cross-document event coreference resolution in Dutch, comparable in size to the most widely used English event coreference corpora. As data for event coreference is notoriously sparse, we took additional steps to maximize the number of coreference links in our corpus. Due to the complex nature of event coreference resolution, many algorithms consist of pipeline architectures which rely on a series of upstream tasks such as event detection, event argument identification and argument coreference. We tackle the task of event argument coreference to both illustrate the potential of our compiled corpus and to lay the groundwork for a Dutch event coreference resolution system in the future. Results show that existing NLP algorithms can be easily retrofitted to contribute to the subtasks of an event coreference resolution pipeline system.
A Compendious Research on Big Data File Formats

In modern times, the net amount of data generated is significantly higher than in previous times, and such data is enormous, complex and hard to manage. The field which deals with such kind of data is known as Big Data. Big Data has helped people understand and get a much better grasp of such complex data. It has also helped extract and analyse information from such data. Big Data is a prominent and promising field, and the three essential factors of big data are volume, variety, and velocity. Big data can be stored in different file formats. File formats play a vital role in big data, influencing the two most essential factors, volume and velocity. The file formats have to be chosen very carefully as they have a domino effect over the speed or velocity of the particular tasks that have to be carried out on the big data. So, a comprehensive study of the following formats CSV (Comma-Separated Values), JSON (JavaScript Object Notation), Avro, Parquet, ORC (Optimized Row Columnar) and XML (Extensible Markup Language), has been carried out. The quantitative analysis has been carried both on premise and on the cloud.
Flight Control System Simulation for Quadcopter Unmanned Aerial Vehicle (UAV) based on Matlab Simulink

Abstract Unmanned aerial vehicle (UAV) are widely used in military and civilian applications due to its advantages of strong maneuverability. Flight control system simulation of a UAV is always a research focus. At present, there are three major algorithms for a UAV flight control: strapdown inertial navigation system, Kalman filter algorithm, flight control Proportional-Integral-Derivative (PID) algorithm. In this study, UAV flight control system and quadcopter drone flight control principle are discussed. In addition, this research developed a dynamic model based on a thorough examination of the quadcopter drone’s frame construction and features. This dynamic model describes the transformation matrix of UAV coordinate system to ground coordinate system. Matlab Simulink was used to simulate a flight control system. A quadcopter drone’s vertical movement, yaw motion, pitching motion, and roll motion, as well as its flight attitude, were simulated and described. Dynamic modeling is developed based on the drone’s flight principle and force relationship in different motion states, as well as the Newton-Eulerian model. Matlab Simulink was used to simulate the trajectory of a quadcopter drone. The red line denotes the trajectory, while the black x markings denote a change in trajectory or a specific position. Blue lines that reflect the waypoint’s heading accompany specific poses. The results of this study could provide a good reference for the simulation of a UAV flight control system.
Automatic arabic text summarization (AATS): A survey

Due to the obvious significant expansion in the number of online Arabic textual information, Arabic Text Summarization has become a focus of intense research. Manual text summarization necessitates a large investment of time, effort, and money. Hence, Automatic Arabic Text Summarization (AATS) is currently necessary to create accurate and relevant summaries from the huge amount of accessible content. The developed techniques and methodologies for AATS are still in their immaturity because of the intrinsic complexity of the structure and morphology of the Arabic language. AATS methods could be categorized as extractive, abstractive, hybrid extractive to abstractive. The extractive method selects and combines the most important sentences from the input document(s) to produce the summary. While the abstractive method needs deep understanding of the input document(s) for creating the summary with sentences that differ from the original ones. The extractive to abstractive method is a hybrid strategy for creating an informative and cohesive summary using the extractive summary as a first step. This paper provides a detailed explanation of the fundamental issues related to Arabic text summarization. It describes and analyzes the various methods and systems currently in use, traces their history and monitors their performance. The challenges and trends are explored.
A scalable Bayesian framework for large-scale sensor-driven network anomaly detection

Many real systems have a network/graph structure with many connected nodes and many edges representing deterministic or stochastic dependencies and interactions between nodes. Various types of known or unknown anomalies and disturbances may occur across these networks over time. Developing real-time anomaly detection and isolation frameworks is crucial to enable network operators to make more informed and timely decisions and take appropriate maintenance and operations actions. To monitor the health of modern networks in real time, different types of sensors and smart devices are installed across these networks that can track real-time data from a particular node or a section of a network. In this article, we introduce an innovative inference method to calculate the most probable explanation of a set of hidden nodes in heterogeneous attributed networks with a directed acyclic graph structure represented by a Bayesian network, given the values of a set of binary data observed from available sensors, which may be located only at a subset of nodes. The innovative use of Bayesian networks to incorporate parallelization and vectorization makes the proposed framework applicable for large-scale graph structures. The efficiency of the model is shown through a comprehensive set of numerical experiments.
From Hyper-dimensional Structures to Linear Structures: Maintaining Deduplicated Data’s Locality

Data deduplication is widely used to reduce the size of backup workloads, but it has the known disadvantage of causing poor data locality, also referred to as the fragmentation problem. This results from the gap between the hyper-dimensional structure of deduplicated data and the sequential nature of many storage devices, and this leads to poor restore and garbage collection (GC) performance. Current research has considered writing duplicates to maintain locality (e.g., rewriting) or caching data in memory or SSD, but fragmentation continues to lower restore and GC performance. Investigating the locality issue, we design a method to flatten the hyper-dimensional structured deduplicated data to a one-dimensional format, which is based on classification of each chunk’s lifecycle, and this creates our proposed data layout. Furthermore, we present a novel management-friendly deduplication framework, called MFDedup, that applies our data layout and maintains locality as much as possible. Specifically, we use two key techniques in MFDedup: Neighbor-duplicate-focus indexing (NDF) and Across-version-aware Reorganization scheme (AVAR). NDF performs duplicate detection against a previous backup, then AVAR rearranges chunks with an offline and iterative algorithm into a compact, sequential layout, which nearly eliminates random I/O during file restores after deduplication. Evaluation results with five backup datasets demonstrate that, compared with state-of-the-art techniques, MFDedup achieves deduplication ratios that are 1.12× to 2.19× higher and restore throughputs that are 1.92× to 10.02× faster due to the improved data layout. While the rearranging stage introduces overheads, it is more than offset by a nearly-zero overhead GC process. Moreover, the NDF index only requires indices for two backup versions, while the traditional index grows with the number of versions retained.