Reviewerly/exHarmony huggingface dataset:
On the Scrutinization of the NFT Valuation Factors

Supporting Non-Fungible Token (NFT) Software Development enables the creation and sale of blockchain-based NFTs backed by unique digital or physical assets. Its value classification is important to justify investment in software development. This study surveys the rapid increase in NFT popularity and proposes a methodology to assess the valuation of an NFT and be able to predict the ultimate success of an NFT. The main influential factors identified in the study are the community and scarcity, our result confirms these two main factors that can affect an NFT's valuation, and this poster paper will look in depth at the correlation between the factors and the value of the NFT and provides the future direction of research.
An Experimental Comparison of Clone Detection Techniques using Java Bytecode

It is generally accepted in Software Engineering that code clones – often the result of copy-and-paste of existing code – result in poorer maintainability of software systems. Consequently, a variety of techniques have been devised to detect cloned code in software systems and alert developers of duplicated code. Most techniques operate at the source-code level and require some combination of pretty-printing, tokenization and abstraction in order to improve the comparison of code fragments over purely string-based techniques. Avoiding some of the issues of source-code based approaches, we are investigating the effectiveness of using various similarity measures on Bytecode to identify code clones in Java-based systems in this work. The results of our evaluation on selected Java systems indicate that instruction sequences can be used to effectively detect identical code clones. Especially, we achieved the best performance when using the normalized edit distance among applied similarity measures.
Blockchain based energy transaction in microgrid

The concept of P2P (Peer to peer) energy transfer includes prosumers (producers + consumers) which complements the emerging technology of blockchain. Blockchain use cases are not just confined to crypto currency; it is used in numerous fields including financial services, Internet of Things (IoT) and voting systems. This technology is reshaping the digital world by bringing a new outlook to security, efficiency and stability of systems and databases. This project aims to provide a potential solution of how blockchain facilitates cost reduction when used in microgrids. The energy is transferred from producer to consumer and the additional service charges are eliminated when using the blockchain method. This idea uses a bidding mechanism in the grid so that the producer who gives energy at a minimum cost is automatically mapped to the consumer. The modified model shows an approximate cost reduction (cost for energy consumption) of 24% in our specified use case. The cost is always reduced when using blockchain in any scenario, but the percentage may vary. The smart contract is included in blockchain shared by the participants and keeps immutable transaction records.
A Flexible and Adaptive Hybrid Algorithm for Secure Data Sharing in Cloud Computing

Secure data-sharing in cloud computing is receiving greater attention in information communication systems since it provides users with efficient and effective data transmission. Cryptographic techniques are frequently employed to protect the confidentiality of shared sensitive data. However, data security remains a serious concern in centralized cloud-based data transfers. The key challenge with cloud data-sharing is determining how to authorize and access data securely. A secure data-sharing mechanism in a multi-cloud environment is introduced to address effective fine-grained access control, data privacy, and confidentiality. Fine-grained data access control is possible with Ciphertext Policy Attribute-Based Encryption. Since this encryption method is unsuitable for attribute coordination among numerous users and critical enforcement and attributes abuse difficulties, an additional encryption approach is required. Blowfish’s key scheduling takes a long time relative to other symmetric algorithms, which might be advantageous when brute force assaults are more complex. Combining the Blowfish technique for re-encryption gives the shared data an extra layer of security. There is still a risk of key generation and insider attacks. The proposed method uses an indexed-based file slicing and merging associated with the SHA-256 hash scheme to address these threats. Furthermore, the security analysis and performance evaluation of the proposed method shows that it is both secure and effective.
Bootstrap Generalization Ability from Loss Landscape Perspective

Domain generalization aims to learn a model that can generalize well on the unseen test dataset, i.e., out-of-distribution data, which has different distribution from the training dataset. To address domain generalization in computer vision, we introduce the loss landscape theory into this field. Specifically, we bootstrap the generalization ability of the deep learning model from the loss landscape perspective in four aspects, including backbone, regularization, training paradigm, and learning rate. We verify the proposed theory on the NICO++, PACS, and VLCS datasets by doing extensive ablation studies as well as visualizations. In addition, we apply this theory in the ECCV 2022 NICO Challenge1 and achieve the 3rd place without using any domain invariant methods.
Building English Audio-Visual and Oral Mobile Teaching System Based on Virtual Augmented Reality Technology

Aiming at the problem that the current English textbooks still provide video materials in the form of CD-ROMs, which affects the learning effect, a research method of English audio-visual mobile teaching system based on virtual and augmented reality technology is proposed. The system first builds a recognition map database and stores it in the cloud and names the corresponding video files according to the name of the recognition map, then uses Unity3D to design and render the scene, design the virtual video playback button of the ImageTarget object, and write script code to realize the recognition map database and its corresponding video, access, and finally, generate a user-friendly mobile application. Users only need to point the lens at the book illustration to present the visual effect of superposition of virtual and real and realize the playback of English teaching videos on mobile devices. The results show that compared with the original image grayscale distribution map, the pixels near the 0 value of the pixel distribution map after twice filtering are significantly reduced, which reduces the hole noise of the image, and the original image and the image after twice filtering are not 0. The peak value of the pixel is close, maintaining the detailed characteristics of the image. The application of augmented reality technology to English video teaching enables users to enjoy novel learning methods and an interactive experience combining virtual and real.
Non-Local Parallel Processing and Database Settlement Using Multiple Teleportation Followed by Grover Post-Selection

Quantum information applications emerged decades ago, initially introducing a parallel development that mimicked the approach and development of classical computer science. However, in the current decade, novel computer-science concepts were rapidly extended to the fields of quantum processing, computation, and communication. Thus, areas such as artificial intelligence, machine learning, and neural networks have their quantum versions; furthermore, the quantum brain properties of learning, analyzing, and gaining knowledge are discussed. Quantum properties of matter conglomerates have been superficially explored in such terrain; however, the settlement of organized quantum systems able to perform processing can open a new pathway in the aforementioned domains. In fact, quantum processing involves certain requisites as the settlement of copies of input information to perform differentiated processing developed far away or in situ to diversify the information stored there. Both tasks at the end provide a database of outcomes with which to perform either information matching or final global processing with at least a subset of those outcomes. When the number of processing operations and input information copies is large, parallel processing (a natural feature in quantum computation due to the superposition) becomes the most convenient approach to accelerate the database settlement of outcomes, thus affording a time advantage. In the current study, we explored certain quantum features to realize a speed-up model for the entire task of processing based on a common information input to be processed, diversified, and finally summarized to gain knowledge, either in pattern matching or global information availability. By using superposition and non-local properties, the most valuable features of quantum systems, we realized parallel local processing to set a large database of outcomes and subsequently used post-selection to perform an ending global processing or a matching of information incoming from outside. We finally analyzed the details of the entire procedure, including its affordability and performance. The quantum circuit implementation, along with tentative applications, were also discussed. Such a model could be operated between large processing technological systems using communication procedures and also on a moderately controlled quantum matter conglomerate. Certain interesting technical aspects involving the non-local control of processing via entanglement were also analyzed in detail as an associated but notable premise.
Privacy Enhanced Federated Learning Utilizing Differential Privacy and Interplanetary File System

As the Internet of Things (IoT) grows exponentially, it is becoming deeply embedded in our daily lives. As the quantity and quality of data produced by devices have also gradually increased, there have been increasing attempts to use these useful IoT big data for various applications and to combine IoT with machine learning and deep learning to process a large amount of useful data. However, in the centralized deep learning method, privacy issues have been raised because the server can use personal data collected from the user’s IoT. Due to this reason, Federated Learning (FL) method that can protect users' personal data while doing machine learning has been studied. However, current FL also has the possibility of data poisoning attacks and other problems. Therefore, this work, by suggesting distributed FL framework combined with Interplanetary File System and Differential Privacy, proposes a method that allows users to participate in FL safely and efficiently. Through this method, participants share some parts of data, and these data are collected by specific nodes. These data are combined to make a new dataset of FL network for defending against data poisoning attack and vouch for training’s accuracy. Also, an aggregation mechanism is proposed to suppress the effect of a malicious node poisoning attack. Finally, this framework is tested in python environment. With this method, one can freely open a project and anyone can join in with distributed condition, even when he or she has no enough dataset for learning but computing capability, vice versa. If a malicious node tries to interrupt the learning with poisoned dataset, aggregation mechanism and combined validation set from the network’s nodes will suppress the bad effect. We have tested through python and open-source code to verify the efficiency and privacy.
The Analysis of Synonymy and Antonymy in Discourse Relations: An Interpretable Modeling Approach

Abstract The idea that discourse relations are interpreted both by explicit content and by shared knowledge between producer and interpreter is pervasive in discourse and linguistic studies. How much weight should be ascribed in this process to the lexical semantics of the arguments is, however, uncertain. We propose a computational approach to analyze contrast and concession relations in the PDTB corpus. Our work sheds light on the question of how much lexical relations contribute to the signaling of such explicit and implicit relations, as well as on the contribution of different parts of speech to these semantic relations. This study contributes to bridging the gap between corpus and computational linguistics by proposing transparent and explainable computational models of discourse relations based on the synonymy and antonymy of their arguments.
Strategy Analysis of Fresh Agricultural Enterprises in a Competitive Circumstance: The Impact of Blockchain and Consumer Traceability Preferences

Blockchain technology allows fresh agricultural enterprises to share records stored on the chain, and the technology can benefit information management systems, such as decentralization and transparency. This study uses game theory to examine a blockchain introduction strategy for fresh agricultural enterprises in a competitive environment, considering consumer traceability preferences. We establish a pricing decision model in traditional and blockchain traceability modes and identify optimal solutions. Additionally, we analyze the impact of the blockchain introduction strategy, consumer preferences, and blockchain influence factor on optimal pricing decisions. The results indicate that the introduction of blockchain could improve the profits of enterprises under certain conditions. Moreover, consumer traceability preferences and the blockchain influence factor could significantly affect the blockchain introduction strategy. We also discover that when the blockchain influence factor meets a certain range, introducing blockchain technique in the traceability system could shift demand from traditional enterprises to blockchain enterprises. The total market demand for blockchain enterprises under the blockchain traceability mode will increase, whereas that of traditional enterprises under the blockchain traceability mode will decrease. Both consumer traceability preferences and the blockchain influence factor could significantly affect optimal pricing. Finally, some management suggestions are provided for the traceability of fresh agricultural enterprises based on the research conclusions.
Language-Guided Face Animation by Recurrent StyleGAN-Based Generator

Recent works on language-guided image manipulation have shown great power of language in providing rich semantics, especially for face images. However, the other natural information, motions, in language is less explored. In this paper, we leverage the motion information and study a novel task, language-guided face animation, that aims to animate a static face image with the help of languages. To better utilize both semantics and motions from languages, we propose a simple yet effective framework. Specifically, we propose a recurrent motion generator to extract a series of semantic and motion information from the language and feed it along with visual information to a pre-trained StyleGAN to generate high-quality frames. To optimize the proposed framework, three carefully designed loss functions are proposed including a regularization loss to keep the face identity, a path length regularization loss to ensure motion smoothness, and a contrastive loss to enable video synthesis with various language guidance in one single model. Extensive experiments with both qualitative and quantitative evaluations on diverse domains ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> human face, anime face, and dog face) demonstrate the superiority of our model in generating high-quality and realistic videos from one still image with the guidance of language. Code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/researchmm/language-guided-animation.git</uri>
Sentimental Analysis on Quick Commerce Platform Based on Customer Reviews in Google Play Store

Quick commerce, commonly referred to as q-commerce, is a distinctive and developing business model in which the time between placing an order and receiving it is less than with e-commerce. This study aims to analyse the customer emotions using sentiment analysis among quick commerce users. The study found six major sentiments of the customers, they are constraining, litigious, negative, positive, superfluous, uncertainty. Data for this study has been extracted from google play store with the help of web scrapping, reviews of 3027 were gathered for this study. The results of this study demonstrate the value of review for analytics and demonstrate the power of sentiment analysis in deriving business value and competitive advantage.
A Delegated Quantum Approximate Optimization Algorithm

The famous quantum approximate optimization algorithm (QAOA) can be an efficient way to solve combinatorial optimization problems which are very useful for various applications such as network analysis and image segmentation. However, its implementation needs a quantum computer. Fortunately, blind quantum computing (BQC) can allow a user with limited quantum ability to delegate the computation to a quantum server and still keep his input, algorithm and output private. In this paper, we improve one typical BQC model for users who only need the ability to perform single-qubit measurements by utilizing an efficient way to generate quantum resource states and thus reduce the quantum memory required by the quantum server. Then by combining the improved BQC model and QAOA, we propose a delegated QAOA where users only with the ability to implement single-qubit measurements also can complete QAOA with the help of a remote quantum server while ensuring the security of their own data.
An Accurate Earthquake Localization Algorithm Using the TDOA Measurements between P and S Waves

This paper investigates the problem of earthquake localization based on the time-difference-of-arrival (TDOA) measurements between P and S waves. The measuring method not only offers the advantage of easy implementability but also effectively lowers the error caused by insufficient understanding of crustal structure. Combining semidefinite programming (SDP) technique and Picard iteration method, an effective algorithm is proposed to accurately locate earthquake focus. Firstly, we derive a nonconvex estimator from the maximum likelihood estimator (MLE), then apply the SDP technique to the derived nonconvex estimator and develop a convex estimator. To further improve the location performance of the focal depth, we append a Picard iteration method to the convex estimator with the result by the convex estimator as the initial value. The proposed algorithm is then extended to the more general case when propagation speed is unknown. The corresponding Cramér-Rao lower bounds (CRLBs) are derived as performance benchmarks. Finally, simulation results show that the proposed algorithm outperforms the existing approaches and attains the CRLB.
Semi-supervised detection of Long Method and God Class code smells

Code smells are poorly designed parts of code whose removal is essential for sustainable software development. However, recognizing code smells in practice is challenging. Machine Learning (ML)-based code smell detectors could solve this problem. Current ML-based code smell detection approaches are based on supervised learning (SL) that requires a large and diverse dataset for training. Unfortunately, the existing code smell datasets are small, which hinders the performance of the trained SL models. This paper aims to improve the performance of ML-based code smell detectors by employing semi-supervised learning (SSL). SSL models are trained by combining a manually labeled code smell dataset with unlabeled code snippets collected from open-source repositories. Two major SSL techniques are employed: self-training and co-training. Experiments were performed for two code smell types: God Class and Long Method. SSL classifiers significantly outperformed SL classifiers for God Class detection (by 6% F-measure). For Long Method detection, SSL classifiers slightly outperformed SL classifiers (by 1%F-measure). This paper is the first to consider applying SSL for code smell detection. SSL models outperforming SL models in all experiments suggest that SSL holds the great potential to improve current code smell detectors, which is essential for their adoption in practice.
IMMERSIVE EDUCATION IN SMART EDUCATIONAL BUILDINGS

This paper is focused on a new way of learning, based on the concept of immersive education, close related to augmented and virtual reality technologies. These technologies allow young people to experiment, learn and understand many subjects, even in the absence of the supporting physical infrastructure. The concept of immersive education can be implemented using a smart educational building. This approach is explained in the paper, using as support the augmented reality and indoor positioning methods. These two main technologies enable teachers and students to experiment a non-traditional way of learning, by using an application which displays interactive content depending on the location of the user: if the student is in the biology lab room, different content regarding biology will be displayed; if the student changes the lab room with the chemistry one, the augmented reality will be full of chemistry experiments, substances and formulas. The goal is to make students learn school subjects with pleasure, to make them understand easily concepts that sometimes cannot be experimented in reality. The mobile application localizes the user in the indoor environment and further augments the reality with different objects, related to the user's position. When computing the position, the orientation is also a key in this solution, because depending on the orientation, the user would be able to see the object from any position. The paper gives some details about the proposed indoor positioning method, justifying the approach, as well as different aspects related to the importance of immersion in this kind of education.
An efficient mixed attention module

Abstract Recently, the application of attention mechanisms in convolutional neural networks (CNNs) has become a hot area in computer vision. Most existing methods focus on channel attention or spatial attention. Some mixed attention usually achieves better performance than channel attention or spatial attention with the help of a complex model structure, which increases the complexity of the model. This article proposes an efficient mixed attention that combines channel information with spatial information using learnable broadcast addition to reduce this complexity. In particular, this module can simplify learning and improve performance with fewer parameters. Furthermore, our method uses an excitation method based on the Tanh function to reduce computational resources while maintaining model performance, and it is a lightweight attention module that can be used in arbitrary CNNs to improve performance. Experiments on ImageNet and Cifar confirm the effectiveness of the proposed method. Besides, our method remains highly competitive for object detection tasks and image segmentation tasks.