Reviewerly/exHarmony huggingface dataset:
Modeling the dynamics of contractual relations

Contracts usually have clauses that enable contracted parties to adjust their contractual positions in time, e.g., to relieve another party from duty or to grant new permission. This is especially important in long-running service relations, which require contracts to be adjusted to accommodate new or unforeseen circumstances. Despite that, the representation of dynamic aspects of contractual relations has not been given enough attention in the literature. In this study, we address this gap by employing the notions of legal power and legal subjection. We propose an ontological analysis of unilateral contractual changes based on a well-founded legal core ontology that adopts a relational perspective for legal positions. We present a case study to show the benefits of representing different types of contractual changes and how these changes can impact contractual dynamics. The case study is based on recent changes to WhatsApp terms of service.
Analytic expressions of the generalized discord for three qubits under local noisy channels

The generalized quantum discord by Radhakrishnan et al. (2020) is analytically evaluated for three-qubit states under local noisy channels. It is demonstrated that such discord can exhibit freezing under the presented conditions.
(Hash)tagging intersection(ality): Black and Palestinian experiences on Twitter

Abstract In this article, we examine how Twitter users discuss intersections of the Black American and Palestinian experience in 2021 through the lens of intersectionality. We explore two questions; how is intersectionality discussed and performed by Twitter users in relation to the Palestinian and Black experience against the backdrop of this particular crisis in Gaza? And how do users engage with the language of intersectionality to either reify, contradict, or complicate the intersection of the Palestinian and Black experiences on the platform? We find that intersectionality is mediated by elite users via branded communication, as well as invoked to highlight or deny the intersections of the Black and Palestinian experience by the most peripheral users on the platform.
Shift‐invariant universal adversarial attacks to avoid deep‐learning‐based modulation classification

Summary With the development of deep‐learning technology, how to prevent signal modulation from being correctly classified by deep‐learning‐based intruders becomes a challenging issue. Adversarial attack provides an ideal solution as deep‐learning models are proved to be vulnerable to intentionally designed perturbations. However, applying adversarial attacks to communication systems faces several practical problems such as shift‐invariant, imperceptibility, and bandwidth compatibility. To this end, a shift‐invariant universal adversarial attack approach is proposed in this work for misleading deep‐learning‐based modulation classifiers used by intruders. Specifically, this work first introduces a convolutional neural network (CNN)‐based UAP (universal adversarial perturbation) generation model that contains an finite impulse response (FIR) filter layer to control the bandwidth of the output perturbation. Besides, this work proposes a circular shift scheme that simulates the random signal cropping in the inference phase and thus ensures the shift‐invariant property of adversarial perturbations. In addition, this work designs a composite loss function that improves the imperceptibility of the adversarial perturbation in both time and frequency domains without decreasing the effectiveness of the adversarial attack. Experimental results demonstrate the effectiveness of the proposed approach, achieving about 50 % accuracy drop on the target model when the perturbation‐to‐signal ratio (PSR) is −10 dB. Furthermore, extensive experiments are conducted to validate the shift‐invariant, imperceptibility, bandwidth compatibility, and transferability of the proposed approach for modulation classification tasks.
Activity Provenance for Rights and Usage Control in Collaborative MR using Blockchain

Collaborative Mixed Reality (MR) provides a virtual/real world in which various users can interact and build virtual objects together. One of the issues in collaborative MR is permission and rights control that defines which user can access which virtual object. A user may define access rights for other users to access the virtual objects created by her/him, which might enable the other users to manipulate the existing virtual object, delete them, or even steal them. For maintaining accountability in the collaborative world, each activity of each user should be maintained against each object. Hence, an activity provenance mechanism is required for a collaborative MR environment, which can be used to identify the culprit. In this paper, a secure activity provenance mechanism for collaborative MR environments is proposed which utilizes blockchain technologies to maintain the integrity of the provenance data. The proposed scheme is implemented on a consortium blockchain using Hyperledger Fabric as a proof of concept. The presented mechanism is also evaluated using metrics throughput and latency and the results confirm that the proposed solution is best suited for maintaining accountability in a collaborative mixed reality network.
The Redefinition of mHealth Applications in the Metaverse

The term "mobile health" (sometimes spelled "mHealth" or "m-Health") refers to the delivery of medical services via smartphones, tablets, PDAs, and PCs. The metaverse combines the real world and the virtual world, allowing people to interact with their avatars in a setting supported by cutting-edge technologies like high-speed internet, virtual reality, augmented reality, mixed reality, extended reality, blockchain, digital twins, artificial intelligence (AI), all of which are enhanced by practically limitless data. This paper discusses how these technologies might be used in digital medicine in the future, as well as the potential of the medical metaverse. This qualitative study examines and evaluates past articles and websites. The healthcare business depends heavily on physical connection, eye contact, facial expressions, and gestures, which the metaverse can simulate virtually. However, the metaverse may be viewed as a tool to improve the effectiveness of the healthcare system in terms of intervention and treatment, worldwide education, assuring consistent training, and assisting in the development of global databases for research. Finally, the metaverse may be a location where young people can start practicing and acquiring new skills considering how much time they spend in front of screens.
SWT Based heterogeneous features to detect Spliced images

Today, unauthorized users intercepts images and modify those using splicing techniques which cannot be perceived by human eyes. This has become a common problem in human lives. It necessitates to design a robust expert system to validate the image authenticity considering the advantage that the splicing introduces some amount of distortion that can be used to detect forged tampered images. The proposed method considers both the statistical features (SF) and the textural features (TF) from upper level bands of stationary wavelet transform (SWT) using HAAR mother wavelet decomposed to first level. The frequency domain features are extracted in multi scale form sub bands of SWT. Markov model (MM) based SF’s are extracted from bands other than the low frequency band to obtain the transition probability matrices which are used then to extract the Haralic’s TF resulting from gray level co-occurrence matrices (GLCM). All the features are then concatenated to form an input vector for the classifier. The combination of TFs and SFs for classification produces significant results when used with Support Vector machine (SVM). The system provides a robust performance for different attacks. The expert systems can be tested for various components including Cb, Cr and mean of Cb-Cr with varying threshold with T=2, 3 and 4. Also, the performance can be evaluated by considering various mother wavelets. Finally, the proposed expert system differentiates good and bad images with SVM. The metrics are evaluated on CASIA v1.0 dataset using hybrid feature set obtained from the SFs and TFs. We obtained an accuracy of 99.30% which outperformed other existing approaches.
An Empirical Study on Blockchain Technology- Enabled Systems in Commodity Markets

The significance of blockchain technology and its applications in commodity markets have been gaining not only recognition but also popularity over the past few years. One of the main reasons behind the acceptance of blockchain technology in commodity markets is efficiency. The concept of centralization has been radically transformed because of blockchain technology. Decentralization, tamperproof nature, transparency, peer-to-peer (p2p) exchange, amongst others are some of the characteristics of blockchain technology that help in tackling issues faced by commodity markets. This study aims to present an empirical study of commodity market-based systems that enable the use of blockchain technology in various business processes.
Object Detection using YOLO to Search Project Details

whenever anyone wants to arrange any exhibition such as painting, science or project, it requires a good number of coordinators to describe an individual’s painting or projects details to the visitors. This manual process has many limitations like arranging of multiple coordinators with the capability of delivering information about a particular project effectively. To overcome this problem, an android based application name Innovation Gallery is proposed, which helps visitors to get the information related to that project as soon as they place the camera in front of object. Before visiting the gallery or exhibition, visitors need to install the proposed application on android mobile phone. Once they place the camera on the respective project of the gallery, it displayed the information related to that project on the mobile screen as augmented text. The proposed approach developed using the YOLO algorithm and successfully tested on image datasets of gallery projects. The given approach is compare with Unity for a similar type of image dataset. It performed well in terms of accuracy, but somewhat lagging in terms of speed.
FPGA-Supported HDL Approach to Implement Reversible Logic Gate-Based ALU

This manuscript banks on the design of reversible gates and implementation of an Arithmetic Logic Unit – 16 bit (ALU) utilizing Verilog with Xilinx ISE 14.7, Spartan 6FPGA kit. The same functionality is compared with a basic logic gate- based ALU. Reversible gates can produce a distinct output vector from each input vector, and the opposite is also possible. Circuits with irreversible gates suffer from data erosion. Power loss results from a circuit’ s loss of data. In conclusion, gates with reversible logic are preferable over irreversible counterparts. A library of reversible gates, comprising of AND, OR, NAND, NOR, and XOR, using Verification Logic Hardware Description Language (HDL) is developed, which in turn contributes to the designing of arithmetic and combinational logic like full adder, decoder (2: 4), decoder (3: S), multiplier, full subtractor, and comparator.
Real Time AI Based Face Mask Detector

The present Covid-19 pandemic, face mask detection identifying significant forward movement in the fields of image and computer observation. Several face detection models were developed utilizing various methods and techniques. The dataset arrangement supplied in this work, which was gathered from multiple sources, could be utilized by other to develop more complex representation such as those for facial identification software, facial positions, and facial component identification. The goal of project “Real Time AI Based Face Mask Detector”, It is develop a tool that really can identify a person image and to affect whether he or she is wearing a mask. COVID makes it necessary to wear a face mask to keep it safe. As the country begins to reopen in stages, face masks have become a crucial part of our everyday life to keep safe. Face masks will be essential for socializing and conducting business. As a result, this software uses a camera to notice whether a person is wearing a mask or not.
Design of IsoQER Cryptosystem using IPDLP

The suggested IsoQuadratic Exponentiation Randomized isocryptosystem design is the unique approach for public key encipher algorithm using IsoPartial Discrete Logarithm Problem and preservation of the recommended IsoQuadratic Exponentiation Randomized isocryptosystem be established against hardness of IsoPartial Discrete Logarithm Problem. Therewith, we demonstrated the possibility of an additional secured algorithm. The offered unique IsoQuadratic Exponentiation Randomized isocryptosystem is suitable for low bandwidth transmission, low storage and low numeration in cyberspace.
Scrutiny For Cybernated Embezzlement Of Credit Card In Modern Era Based On Machine Learning Using Smote &amp; AdaBoost

Credit card embezzlement take place often and result in the huge capital loss. Credit card embezzlement has ramped up dramatically as a result of modern digitalization and modern communication expressways. It is vital to build systems to assure the security of credit card transaction. Using real world imbalanced dataset induced from European credit cardholder, we constructeda ml based model for detecting credit card embezzlement. Utilizing the synthetic minority over sampling technique, we resampled the data set to solve the class imbalance issue. The following algorithms were adopted to determine this framework: support vector machine, logistic regression, random forest, extreme gradient boosting, decision tree. These machine learning methods were integrated with adaptive Boost technique to foster classification quality. The styles were assessed using accuracy, recall, precision, Matthews correlation coefficient, and area under the curve (AUC). The results of the experiments show that AdaBoost increases the performance of the offered approaches. Furthermore, we proposed that the card details should vanish or be removed from the merchant gateway after the completion of the transaction. If the payment without online transaction processing (OTP) is encountered, then it should be flagged as an embezzled transaction. If a third party other than then cardholder uses the credit card for pay rent option to transfer the amount to a respective bank account illegally, then it should be reported as embezzlement. Thee pay rent amount should not exceed 25000. If the FEMA(Foreign Exchange Management (Act, 19999) options display during a transaction, the user should be informed that services including purchasing lottery tickets, forex trading, call back services, beating swap stacks, gambling transactions, and forbidden periodicals are not allowed and the transaction should be declined automatically.
Brain Tumor Segmentation and Model Optimization for 3-D Printing

Medical image acquisition is generally done through Magnetic Resonance Imaging (MRI) or computerized tomography (CT) scan which is a non-intrusive technique. For identifying the Tumor in the brain, a segmentation process is used which involves using image processing algorithms on MRI scans. While many approaches have been used for brain tumor segmentation, the manual segmentation process is tedious and has inherent problems associated with it. This paper proposes an implementation of3-D slicer segmentation of the brain tumor model along with STL model optimization using Autodesk Netfabb. This will provide an effective way of reducing the STL errors in the segmented 3-D models. These optimized models can then be 3-D printed which will provide accurate information on tumor shape and size to the medical professionals. The process of STL model optimization shows very promising results on the MRI dataset used in this case study and it achieves the creation of error-free STL models for 3-D printing. Additionally, this work demonstrates the effective use of the 3-D slicer along with Autodesk NetFabb, to considerably reduce the STL errors which were generated in the conventional process of manual segmentation. The manual segmentation process was tedious and required force smoothening of 3-D slicer models obtained after segmentation leading to faulty 3-D model printing.
Blockchain Application on Big Data Security

In recent years, advances in technology in several industries have resulted in massive data collections on the web. It raises worries about large data security and protection. The advent of Blockchain technology has caused a revolution in the security field for different applications. The distributed ledger is stored on each Blockchain node, which enhances security and data transparency. On the Blockchain network, illegal users are not authorized to undertake any fault transactions. In this article, we will discuss how Blockchain may be employed to secure the big data. We explain the problems that the Blockchain faced with big data and its solutions. We summarize recent works of Blockchain with big data and the present issues and trends. We demonstrate that Blockchain technology is still in its initial phases of validation and there are no large-scale application scenarios available, particularly in the big data sector. Finally, we narrow our study to the Healthcare industry and offer the following research directions for its primary issues.
Opinion Mining on Amazon Musical Product Reviews using Supervised Machine Learning Techniques

People these days express their sentiments about a specific product through social media and networking websites. Sentiment Analysis or Opinion Mining is the analysis of such sentiments from texts, which uses natural language processing. Opinion mining on Amazon musical product reviews identifies these sentiments by classifying them as a positive or a neutral or a negative polarity. This will also play a vital role in the decision making and recommendation of products by understanding the polarity of the reviews. In this proposed work, the Machine Learning feature extractions (TF-IDF and Bag of Words) give a much better accuracy when tested against 6 classifying algorithms; Naïve Bayes, Logistic Regression, Decision Tree, K-Nearest Neighbour, Random-Forest and SVM, and 2 neural networking algorithms; Artificial Neural Network and Recurrent Neural Network. The proposed work will help in distinguishing between positive, neutral and negative reviews accurately. Therefore, a large dataset of more than 10000 reviews, with an average of 300 words per review, is used to serve as a stronger model. During implementation, a maximum accuracy of 97.7% was implemented when tested using the SVM algorithm, and a minimum accuracy of 81.3% was implemented when tested using the Decision-Tree algorithm.
Chaotic Sampling of Double Scroll Chaos for Digital Random Number Generation

This article introduces a random number generator based on chaotic oscillators. Digitally obtained double scroll chaos was used as the basis for RNG design. The RNG is implemented on FPGA at the register transfer level using the third order ordinary differential equation to generate double scroll chaos. The 5-bit signed integer of a 32-bit fixed point number was used in the implementation. A signal that featured chaotic characteristics was used as a source and another chaotic signal was employed to sample the source signal to generate random bits. The random number generator that has been suggested is built using Verilog hardware description language and experimentally demonstrated on a Xilinx Virtex VC707 FPGA. The collected binary bits were subjected to the FIPS 140–2 randomness test suite and passed the tests successfully.
“Fusion, Interaction, and Training”: Cultivating Innovative Leading Talents

Engineering science and technology is the engine of human progress and a powerful lever for industrial revolution, economic development and social progress. Relying on the construction of “New Engineering”, School of Electronic Science and Engineering of University of Electronic Science and Technology of China (UESTC) take full advantage of the advantages of electronic information technology, carries deep research on education and teaching reform for a long time, innovative educational framework of “Fusion, Interaction, and Training” has been proposed, enhance the training of innovative talents in electronic information era, and has achieved a series of practical results. Recently, the Ministry of Education issued the “Decision on the 2018 National Teaching Achievement Award Winning Project”, School of Electronic Science and Engineering of UESTC has achieved the national second prize. This award highlights that: UESTC has attached great importance to the training of talents, has paid close attention to improvement of teaching quality engineering, the ability and quality of talents, and will accelerate the “Double First-rate” undergraduate education, which is a new ambitious higher education policy that China has officially launched.
Internet of Things Communication protocols optimization using Blockchain Technology integrated with Reinforcement Learning

Under the IoT vision, conventional items become sophisticated and self-contained. This ideal has become an actuality due to its technological breakthroughs, although there are still challenges to face. Especially in the field of security, like data accuracy. All academia and commerce are curious about the combined study of block chain and computational modelling (ML) because it may offer significant advantages for achieving decentralized, safe, intelligent, and advanced information management operations and administration. Considering the expected IoT will have to establish relationships in this massive incoming data stream as it evolves in the coming years Data sharing will change as a result of the crucial software known as the blockchain. A scientific innovation that can transform several industries, such as the Internet of Things, is the capacity to make connections within dispersed systems without the need for power. As a result, the controller operates well and may be modified to fit into different, dynamic contexts. Additionally, since greater-level systems are taught with deep reinforcement learning, the machine can expect to study even after it is put into use, which makes it perfect for practical uses. Based on their personal experience with the employment of an agent, learning through Reinforcement: S (State), A (Action), and R (Reaction) are the parameters (Reward Under the IoT vision, ordinary items become clever and self-contained. This paper investigates this relationship, analyses issues with blockchain and IoT systems, and assesses the most pertinent studies in order to establish how blockchain with reinforcement learning may enhance IoT. Depending on the finding, the study then suggests using supervised learning techniques to address some of the major problems faced by blockchain-enabled IIoT systems, such as block time reduction and operations throughput development. There will be a thorough case study that demonstrates how a Q-learning strategy can be used to minimize latency problems for a miner and hence lower the likelihood of forking events.