Reviewerly/exHarmony huggingface dataset:
“I Can See Your Password”: A Case Study About Cybersecurity Risks in Mid-Air Interactions of Mixed Reality-Based Smart Manufacturing Applications

Abstract This paper aims to present a potential cybersecurity risk existing in mixed reality (MR)-based smart manufacturing applications that decipher digital passwords through a single RGB camera to capture the user’s mid-air gestures. We first created a test bed, which is an MR-based smart factory management system consisting of mid-air gesture-based user interfaces (UIs) on a video see-through MR head-mounted display. To interact with UIs and input information, the user’s hand movements and gestures are tracked by the MR system. We setup the experiment to be the estimation of the password input by users through mid-air hand gestures on a virtual numeric keypad. To achieve this goal, we developed a lightweight machine learning-based hand position tracking and gesture recognition method. This method takes either video streaming or recorded video clips (taken by a single RGB camera in front of the user) as input, where the videos record the users’ hand movements and gestures but not the virtual UIs. With the assumption of the known size, position, and layout of the keypad, the machine learning method estimates the password through hand gesture recognition and finger position detection. The evaluation result indicates the effectiveness of the proposed method, with a high accuracy of 97.03%, 94.06%, and 83.83% for 2-digit, 4-digit, and 6-digit passwords, respectively, using real-time video streaming as input with known length condition. Under the unknown length condition, the proposed method reaches 85.50%, 76.15%, and 77.89% accuracy for 2-digit, 4-digit, and 6-digit passwords, respectively.
A multi-dimensional dataset of Ordovician to Silurian graptolite specimens for virtual examination, global correlation, and shale gas exploration

Abstract. Multi-elemental and multi-dimensional data are more and more important in the development of data-driven research, as is the case in modern paleontology, in which, in an examination by experts, or some day artificial intelligence, every fossil specimen plays a fundamental role. We here release a dataset of 1550 graptolite specimens representing 113 Ordovician to Silurian graptolite species or subspecies that are significant in global stratigraphic correlation and shale gas exploration. The dataset contains 2951 high-resolution images and a data table of each specimen's scientific information, including the taxonomic, geologic, and geographic information, comments, and references. Our dataset provides images for specialists or laypeople worldwide, is supported by the tool FSIDvis (Fossil Specimen Image Dataset Visualizer), which we developed to facilitate the interactive exploration of the rich-attribution image dataset, and includes a nonlinear-dimension reduction technique, t-SNE (t-distributed stochastic neighbor embedding), to project image data into a two-dimensional space to visualize and explore the similarities. Our dataset potentially contributes to virtual examinations of specimens, global bio-stratigraphic correlation, and improvement of the shale gas exploration efficiency. All data, images and the spreadsheet are available at https://doi.org/10.5281/zenodo.6688670 (Xu, 2022).
Improved multi-objective artificial bee colony algorithm-based path planning for mobile robots

Mobile robots are widely used in various fields, including cosmic exploration, logistics delivery, and emergency rescue and so on. Path planning of mobile robots is essential for completing their tasks. Therefore, Path planning algorithms capable of finding their best path are needed. To address this challenge, we thus develop improved multi-objective artificial bee colony algorithm (IMOABC), a Bio-inspired algorithm-based approach for path planning. The IMOABC algorithm is based on multi-objective artificial bee colony algorithm (MOABC) with four strategies, including external archive pruning strategy, non-dominated ranking strategy, crowding distance strategy, and search strategy. IMOABC is tested on six standard test functions. Results show that IMOABC algorithm outperforms the other algorithms in solving complex multi-objective optimization problems. We then apply the IMOABC algorithm to path planning in the simulation experiment of mobile robots. IMOABC algorithm consistently outperforms existing algorithms (the MOABC algorithm and the ABC algorithm). IMOABC algorithm should be broadly useful for path planning of mobile robots.
Impact of Using the Internet of Medical Things on e-Healthcare Performance: Blockchain Assist in Improving Smart Contract

Background: This paper explores the use of blockchain technology and smart contracts in the Internet of Medical Things (IoMT). It aims to identify the challenges and benefits of implementing smart contracts based on blockchain technology in the IoMT. It provides solutions and evaluates the IoMT uses in e-healthcare performance. Methods: A quantitative approach used an online survey from public and private hospital administrative departments in Dubai, United Arab Emirates (UAE). ANOVA, t -test, correlation, and regression analysis were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain). Patients and Methods: A mixed method was used in this research, a quantitative approach for data analysis utilizing online surveys from public and private hospitals’ administrative departments in Dubai, UAE. A correlation, regression through ANOVA, and independent two-sample t -test were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain). Results: Blockchain application in smart contracts has proven to be significant in the healthcare sector. Results highlight the importance of integrating smart contracts and blockchain technology in the IoMT infrastructure to improve efficiency, transparency, and security. The study provides empirical evidence to support the implementation of smart contracts in the e-healthcare sector and suggests improved e-healthcare performance through this transition. Conclusion: The emergence of e-healthcare systems with upgraded smart contracts and blockchain technology brings continuous health monitoring, time-effective operations, and cost-effectiveness to the healthcare sector. Keywords: internet of medical things, IoMT, e-healthcare, smart contracts, blockchain
Permissive and regulative norms in deontic logic

Abstract This article provides a systematic analysis of the well-known notions of weak and strong permission in input/output (I/O) logic. We extend the account of permission initially put forward by Makinson and Van der Torre to the whole family of I/O systems developed during the last two decades. The main contribution is a series of characterization results for strong permission, based on establishing the so-called non-repetition property. We also study an input/output logic not yet covered in the literature. It supports reasoning by cases—a natural feature of human reasoning. The output is not closed under logical entailment. At the same time, it avoids excess output using a consistency check—a technique familiar from non-monotonic logic. This makes it well suited for contrary-to-duty reasoning. The axiomatic characterization is in terms of a generalized OR rule. We discuss the implications of all this for our understanding of the notion of the coherence of a normative system. Topics for future research are identified.1
Contrastive Learning with Frequency Domain for Sequential Recommendation

Sequential recommendation has recently played an important role on various platforms due to its ability to understand users’ intentions from their historical interactions. However, modeling user’s intention on time-based representations poses challenges, such as fast-evolving interests, noisy interactions, and sparse data. While contrastive self-supervised learning can mitigate these issues, the complexity of time-based interactions limits the utility of understanding the user’s intention. Motivated by this limitation, we posit that intent representations need to accommodate different domains. To this end, we expect both the frequency-domain augmented view and the time-domain augmented view of the same sample should be maximally consistent with their original input in their corresponding domains. Inspired by this idea, we propose Contrastive Learning with Frequency Domain for Sequential Recommendation (CLF4SRec), where a learnable Fourier layer provides the frequency-based self-supervised signal. Instead of pre-training, we employ a multi-task learning framework jointly with contrastive learning and recommendation learning to optimize the user representation encoder. We conduct comprehensive experiments on four real-world datasets, where CLF4SRec outperforms the recent strongest baselines from 7.58% to 67.85%, showing its effectiveness for sequential recommendation tasks. Specifically, CLF4SRec can achieve a boost of 41.88% to 67.85% on the dense dataset, which might be attributed to the ability of frequency domain technology to handle dense signals. The implementation code is available at https://github.com/zhangyichi1Z/CLF4SRec.
Efficient identity-based multi-copy data sharing auditing scheme with decentralized trust management

The development of cloud storage provides convenient group data sharing services to us. In reality, not only can users upload data to the cloud, but more often they are inclined to download data from the cloud. Unfortunately, data stored on the cloud is not always trustworthy, and downloading Trojan or virus data can bring bad experiences to users. In certain cloud computing scenarios, with the aim of preventing data loss, both the original data and the corresponding data copies are stored. Consequently, the huge amounts of stored data copies result in large storage overhead to the cloud. In order to tackle all of the above problems, an efficient identity-based multi-copy data sharing auditing scheme with decentralized trust management is presented in this paper. In our scheme, the group manager detects shared data and generates short signatures for the data that can pass detection successfully before it is stored on the cloud. Further, the group manager calculates the trust value for users and enhances the management of user behavior according to it. Through adopting data merging, the total amount of data copies and the overhead of users and cloud are reduced in our scheme. In terms of security, our scheme can resist forgery attacks launched by three different adversaries. At last, we prove that our scheme is secure and practical through security and performance analysis, respectively.
Cluster-based ensemble learning model for improving sentiment classification of Arabic documents

Abstract This article reports on designing and implementing a multiclass sentiment classification approach to handle the imbalanced class distribution of Arabic documents. The proposed approach, sentiment classification of Arabic documents (SCArD), combines the advantages of a clustering-based undersampling (CBUS) method and an ensemble learning model to aid machine learning (ML) classifiers in building accurate models against highly imbalanced datasets. The CBUS method applies two standard clustering algorithms: K -means and expectation–maximization, to balance the ratio between the major and the minor classes by decreasing the number of the major class instances and maintaining the number of the minor class instances at the cluster level. The merits of the proposed approach are that it does not remove the majority class instances from the dataset nor injects the dataset with artificial minority class instances. The resulting balanced datasets are used to train two ML classifiers, random forest and updateable Naïve Bayes, to develop prediction data models. The best prediction data models are selected based on F1-score rates. We applied two techniques to test SCArD and generate new predictions from the imbalanced test dataset. The first technique uses the best prediction data models. The second technique uses the majority voting ensemble learning model, which combines the best prediction data models to generate the final predictions. The experimental results showed that SCArD is promising and outperformed the other comparative classification models based on the F1-score rates.
Data transaction privacy protection mechanism based on bulletproof in multi-channel blockchain

In the era of big data, data has gradually become an important asset. In order to fully protect the value of data and ensure the effective conduct of data transactions, this paper combines bulletproof and blockchain to ensure that data transactions can be carried out in a safe and reliable environment, while also hiding the value of data itself. The commitment of bulletproof ensures that all users on the blockchain can verify the validity of the transaction but not the details of the transaction. In this paper, the bulletproof is simulated, and the simulation results verify the feasibility and practicability of the scheme.
Significance of Blockchain Technology in the Healthcare Sector

The healthcare industry produces a lot of data. This vast volume of data, which includes patient medical information and electronic health records is becoming more accessible to hackers on the dark web. All this data is available on the dark web, making the healthcare sector vulnerable. Many methods and strategies are used to protect data; however, they have proven to be more expensive to maintain than expected. Blockchain is one of the technologies that guarantees a solution to every issue as a solution to every problem. There are several existing blockchain technologies that are being used or tested in the healthcare system. One example is the use of blockchain to securely store and share electronic medical records (EMRs) among healthcare providers. This can improve the efficiency and accuracy of patient care by giving doctors and other healthcare professionals quick and secure access to a patient's complete medical history. Another example is the use of blockchain to track and manage the supply chain of drugs, to ensure that they are authentic and have not been tampered with. Other blockchain applications in healthcare include the use of smart contracts for medical claims processing and the use of blockchain-based tokens for incentivizing healthy behavior. Out of the three architectures available in Blockchain technology, private blockchain is selected to ensure a closed network and increased security. The decentralized mechanism ensures that all hospitals in the network have access to the patient. In the IPFS database, the patient data is stored as a block and an electronic health record. The patient can access his or her data using the private key. Private blockchain technology is a type of distributed ledger technology that is only accessible to a specific group of individuals or organizations. This means that the network is not open to the public, and only authorized participants can access and validate transactions on the chain. Private blockchain is often used by businesses and organizations to securely share and access data and assets among trusted parties.
An E-Commerce Product Feedback Review Using Sentimental Analysis

Research in natural language processing and text mining has been active in this area for the past few years. Many companies are using a sentimental analysis approach to get feedback from customers. So, the main objective is to use the result of this analysis to understand and analyse the feedback given by the customers. A website will be used in this study to collect information for sentimental analysis where feedback has been taken from customers by using the website. After, customers fill out the feedback forms. The backend data of customers is stored in MySQL. Data from the website is stored as a CSV file and the CSV file is used to get the analysis result. In this way, an analysis can be done regarding the input given by users, whether they have given the right feedback, and if the product is valuable or not. In this way, an idea regarding customers' way of giving feedback can be analysed. The main challenge is collection of the data for the study. For analysis, many solutions use Naïve Bayes. The main disadvantage is that, here in naive bayes, an assumption has been made based on the probabilities of the logs. In the Naive Bayes rule, the position of words is not considered, so the sentiment cannot be predicted based on the context of a document. The goal of this study is to review previous studies of analysis and examine the best method to compare customer feedback.
Sentiment Analysis using Text And Emoji's

The investigation of the connection between sentiment analysis and emoji translation to text is the main goal of the project. The project's data consists of a range of product review from YouTube that were gathered on the video. In the end, this analysis looks at how emoji usage relates to product reviews that are seen as using natural language with emoji. The natural language processing used for text preprocessing. Emoji can also be considered as one of the languages to express the human express in the context. The advantage of this language is that no need to learn it and anyone can understand it. Everyone can convey their emotions and can also easily understand other's emotions. This research study intends to analyze the emotion of text and emoji-based YouTube comments. Analyzing the comments directed to acknowledge the review of the product in the market. In this project we are going to compare the emoji translated text and original text data.
Tracking and Localization of Devices - An IoT Review

Several IoT applications have immediate impacts on daily lives. The notion of “connected life”, which includes IoT has been discussed. Apps that rely on localization are also featured. IoT is originally used to determine the precise position of things, animals, and people. The second tracks everyone and everything that's on the move, including pets, kids, and the elderly people. Localization and tracking are integral parts of security and surveillance systems in interconnected homes. This study reviews the state-of-the-art IoT-based localization and tracking approaches and outlines the key technical aspects, and contrast localization initiatives based on Internet of Things (IoT) with those that do not show how they might be used in a variety of contexts. It is now well established that localization and tracking methods based on the Internet of Things (IoT) are more pervasive and accurate than their predecessors.
A Tamper-Resistant Storage Framework for Smart Grid security

In the past few years, the energy sector has been among the most targeted by cyber-criminals. Due to the strong reliance of Critical Infrastructures on energy distribution, and the strategic value of such systems, the impact of intrusions and data breaches cannot be underestimated. In this scenario, data constitutes a critical asset to protect, especially as the latest technological development has led to interconnected intelligent systems, named smart grids. The consequences of data tampering, exposure or loss can range from disruption of essential services, to serious risks for environment, economy and people safety. Data provenance, as the documentation of the origin of data and the processes and methodology that led to it, can bring support when facing the aforementioned attacks. The present work aims to address security issues in the energy domain, by proposing the Advanced Tamper-Resistant Storage (ATRS), a novel framework for data provenance based on blockchain technology. The ATRS allows for the creation and storage of provenance records, whose reliability is ensured by the tamper-resistance feature enabled through the combination of blockchain and TLS-based communication. The framework, tailored and tested for the smart grid domain, can easily be customized for different critical use cases.
Content-aware auto-scaling of stream processing applications on container orchestration platforms

Modern applications are designed as an interacting set of microservices, and these applications are typically deployed on container orchestration platforms like Kubernetes. Several attractive features in Kubernetes make it a popular choice for deploying applications, and automatic scaling is one such feature. The default horizontal scaling technique in Kubernetes is the Horizontal Pod Autoscaler (HPA). It scales each microservice independently while ignoring the interactions among the microservices in an application. In this paper, we show that ignoring such interactions by HPA leads to inefficient scaling, and the optimal scaling of different microservices in the application varies as the stream content changes. To automatically adapt to variations in stream content, we present a novel system called DataX AutoScaler that leverages knowledge of the entire stream processing application pipeline to efficiently auto-scale different microservices by taking into account their complex interactions. Through experiments on real-world video analytics applications, such as face recognition and pose classification, we show that DataX AutoScaler adapts to variations in stream content and achieves up to 43% improvement in overall application performance compared to a baseline system that uses HPA.
Stratus: A Hardware/Software Infrastructure for Controlled Cloud Research

Cloud systems deploy a wide variety of shared resources and host a large number of tenant applications. To perform cloud research, a small experimental platform is commonly used, which hides the huge system complexity and provides flexibility. Despite being simpler, this platform should include the main cloud system components (hardware and software) to provide representative results. A wide set of platforms have spread in recent years; however, most of them only include a major cloud component or lack the deployment of virtual machines (VMs) to provide isolation. This paper presents Stratus, an experimental platform that is currently being used to carry out cloud research. To the best of our knowledge, Stratus is the only platform that jointly provides three main features: uses VMs to isolate tenant applications, deploys the three types of cloud nodes (server, client, and storage), and manages all main shared system resources (CPUs, LLC space, memory, network, and disk bandwidth). Moreover, Stratus implements a software manager to ease the research and aid the design of QoS-aware policies. The manager integrates three main functionalities: management and control of the execution of VMs and running applications, monitoring of hardware performance counters and system resource utilization, and partitioning of the main shared system resources by using technologies available in commercial processors.
A Virtual e-Learning Environment Model Based on Metaverse

Metaverse is a new type of social network based on Web3 and has multi-technology, decentralized and hyperspatial temporal features. In the Metaverse world, people play roles online and in 3D in the form of avatars. Therefore, the fields of electronic education can also take advantage of this new potential and provide the path of changes for availability and sustainability in the future. Until now, most of the research regarding the discussion of Metaverse and e-Learning has been around the benefits and the possibility of implementing educational systems on this platform, and they have focused more on the progress that Metaverse has made so far, and an educational system based on Metaverse has not yet been operationally implemented. All items are at the design level and in the best case, only a 3D environment is created to present the content non-dynamically. In this paper, we will present a Metaverse virtual e-Learning environment model, all the basic and infrastructural elements, and we will show the ideal mechanisms and technologies and their challenges. This model will lead to the easy development of Metaverse applications in the field of e-Learning.