Reviewerly/exHarmony huggingface dataset:
Preservation and Reproduction of an Ancient Human Humerus through X-ray Microscopy and 3D Printing

Abstract The combination of X-ray Microscopy (XRM) and three-dimensional virtual reconstruction has enabled the digitization and restoration of broken artifacts. By scanning, acquiring, and virtually stitching together the 3D reconstructions of individual broken pieces, damaged relics can be visualized as if they were intact objects. These virtually reconstructed samples can then be reproduced as physical copies through 3D printing, allowing for the sharing of rare findings in museum exhibits worldwide so that printed copies can be displayed for public exposure, while the original pieces remain preserved. This paper aims to demonstrate the application of these reconstruction principles to an artificially modified human humerus belonging to the II–I millennium BC. The humerus was bent into the shape of a serpent for ritual purposes related to the ancient “Snake Cult”, which was widespread in the Persian Gulf area during the Iron Age. Following the scanning and software elaboration processes, the pieces were printed in Polylactic Acid (PLA) as a single object and made available to the public, thus giving new life to a unique piece of history.
Quantum Fisher information of multi-qubit WV entangled state under Lipkin-Meshkov-Glick model

As an important quantity in the field of parameter estimation theory and quantum precision measurement, quantum Fisher information (QFI) can not only be used to set the theoretical limit of measurement precision in quantum system, but also be exploited to witness metrological useful quantum entanglement. Recently, it has also been broadly used in many aspects of quantum information science, including quantum metrology, multipartite entanglement structure detection, quantum phase transition, quantum chaos, quantum computation and etc. In this work, from the perspective of quantum measurement, we study the quantum Fisher information of an &lt;i&gt;N&lt;/i&gt;-qubit WV state (&lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$\alpha \left\vert W_N \right\rangle +\sqrt{1-\alpha^2}\left\vert 00\cdots0\right\rangle$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20231179_M4.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20231179_M4.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;) under local operation and Lipkin-Meshkov-Glick (LMG) model. Furthermore, with the general Cramér-Rao lower bound (CRLB) we analyze its performance in high-precision phase measurement. The results show that, under the local operation, the QFI of an &lt;i&gt;N&lt;/i&gt;-qubit WV state becomes larger with the increase of parameter &lt;i&gt;α&lt;/i&gt;. This not only means the enhanced quantum entanglement, but also implies the powerful ability in high-precision quantum measurement. In the LMG model, as the increase of interactional strength &lt;i&gt;γ&lt;/i&gt; the QFI of &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$N=3$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20231179_M5.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="22-20231179_M5.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; qubits WV state gradually tends to be stable and almost not be affected by parameter &lt;i&gt;α&lt;/i&gt;, which relaxes the requirement in the preparation of target state and indicates a great potential in achieving the relatively stable measurement precision. When the number of qubits from WV state is larger than 3, the QFI of WV state increases with the increase of parameter &lt;i&gt;α&lt;/i&gt;. In the case of fixed parameter &lt;i&gt;α&lt;/i&gt;, we investigate the QFI of an &lt;i&gt;N&lt;/i&gt;-qubit WV state with respect to interaction strength &lt;i&gt;γ&lt;/i&gt;. It is found that the QFI of WV state will increase with the increasing interaction strength, which implies that the greater the interaction strength, the stronger the quantum measurement ability of the WV state. Our work will promote the development of high-precision quantum metrology and especially the interaction-enhanced quantum measurement, and further provide new insights in quantum information processing.
The Civil Law Protection of Virtual Property Rights in Online Games

In the information age, the influence of the Internet on people's lives is increasing, especially in online games, whose radiation groups include almost all age groups such as teenagers, middle-aged and elderly people. With the increasing amount of time, money and energy people inject in the virtual environment of online games, the value of virtual property in the Internet can no longer be underestimated. Therefore, in the newly promulgated Civil Code of the People's Republic of China, online virtual property is explicitly listed as an object protected by law with the same status as substantive property. However, the application of its provisions is too flexible, so this paper puts forward the author's own opinions and suggestions on the legal protection of virtual property in the present-day context, starting from the value of virtual property on the Internet, the doctrines of various schools of thought and the legal protection in other countries.
Secure Multiparty Computation with Identifiable Abort and Fairness

Dishonest majority considered in the SPDZ(the nickname of the protocol of Damgard et al. from Crypto 2012) protocols implies the impossibility of fairness(which means that corrupted parties can prevent the honest parties from learning output). The corrupted parties can learn the outputs of the honest parties and abort the protocol. Settling for the second best, there are many works focusing on the detection of the cheaters. We construct a SPDZ-like protocol which achieves fairness when at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n/2$</tex> parties behave maliciously and supports identifiable abort for dishonest majority. We suggest a sharing stage after the parties finish their computation. The parties share the returns of the computation in this stage. The correctness of the sharing is guaranteed by verifiable secret sharing and homomorphic signature. The honest parties can reconstruct the outputs of the cheaters in the setting of an honest majority. We can't prevent the corrupted parties from learning the outputs and aborting the protocol for dishonest majority. Therefore, the sharing stage does not harm to the honest parties. Instead, we provide the honest parties with the identities of all cheaters in this case.
Heterogeneous Federated Learning with Cross-layer Model Fusion

Federated Learning (FL) is a distributed machine learning framework that enables clients to train models using local datasets while preserving data privacy. However, FL faces challenges due to the complexity and diversity of data sources, computing resources, and client device capabilities, resulting in data and model heterogeneity. We propose an innovative FL framework called heterogeneous federated learning with cross-layer model fusion (HFedCMF) to address this. In this framework, clients upload their heterogeneous local models to the server, which then obtains a new global model using the cross-layer model fusion method that exploits the optimal transport properties. Additionally, we introduce a modified group lasso regularization in local training to separate the training of personalized models from the global fusion process, enhancing clients’ ability to achieve superior personalized performance. Experiments on various datasets demonstrate that the proposed HFedCMF outperforms homogeneous and heterogeneous FL algorithms.
A Routing Scheme Based on Multi-Objective Optimization in Quantum Satellite Networks

In quantum satellite networks, routing is an important task for providing key relay services to ground stations (GSs). For the demand of quantum keys for multiple concurrent requests between GS pairs, we formulate a multi-objective optimization routing (MOOR) problem to allocate the quantum key and time-slot resources to find routes for simultaneous services. The proposed optimization scheme maximizes the number of successful requests while minimizing the routing cost. Specifically, we consider that the fewer available quantum keys on the link, the greater the cost of occupying the link. Then we use the reciprocal of the number of available keys on links to represent link costs, and find the route with the minimum link costs. Simulation results show that the success probability of MOOR scheme is higher than that of the comparison routing strategy based on Dijkstra algorithm, especially when the number of keys forwarded by a request is not too large, the improvement is significant.
An Efficient Public Key Encryption With Set Equality Test

Users can compare the equality of two encryption texts encrypted by different public keys in the public key encryption with equality test (PKEET) scheme. And the encryption texts do not have to be decrypted. Actually, users may need to verify the equality of two ciphertext sets. A trivial solution using typical PKEET scheme is testing two ciphertexts respectively from two sets and repeating multiple times until all ciphertexts are tested. Obviously, the above solution not only spends a lot of time, but also discloses the equality between any two ciphertexts in the two sets. For fear of the aforementioned issue, we propose an efficient public key encryption with set equality test (PKE-SET). PKE-SET does not use expensive bilinear pairings. Experimental results indicate that, compared to other PKEET schemes, our PKE-SET has higher computational efficiency and only our PKE-SET does not disclose the equality between any two ciphertexts.
ZREC: Robust Recovery of Mean and Percentile Opinion Scores

Observer screening and subject opinion score recovery is essential for collecting a reliable QoE database. This paper proposes a new method, ZREC*, which uses Z-scores to estimate subject bias, inconsistency, and content ambiguity. Additionally, we propose Mean Opinion Score (MOS) recovery and Percentile Opinion Score (POS) recovery scheme based on the three estimated parameters. ZREC does not fully reject subjects, rather adjust their coefficients in the MOS/POS recovery, allowing for more efficient use of data collection. The estimated parameters of ZREC are highly correlated with more complex solver-based methods and standards. In addition, ZREC recovers MOS with smaller confidence intervals than the state of the art. Experimental results also demonstrate that using recovered p <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</inf> POS as ground truth during training improves the performance of Satisfied User Ratio (SUR) prediction.
Modeling of intelligent hyperparameter tuned deep learning based copy move image forgery detection technique

Due to the drastic increase in the generation of high-quality fake images in social networking, it is essential to design effective recognition approaches. Image/video manipulation defines any set of actions which can be carried out on digital content by the use of software editing approaches or artificial intelligence. A major kind of image and video editing comprises replicating the regions of the image, named as copy-move technique. Conventional image processing methods physically search for the pattern relevant to the replicated contents, restricting the utilization in massive classification of data. Contrastingly, the recently developed deep learning (DL) models have exhibited promising performance over the traditional models. In this aspect, this paper presents a novel intelligent deep learning based copy move image forgery detection (IDL-CMIFD) technique. The proposed IDL-CMIFD technique intends to design a DL model to classify the candidate images into two classes: original and forged/tampered and then localized the copy moved regions. In addition, the proposed IDL-CMIFD technique involves the Adam optimizer with Efficient Net based feature extractor to derive a useful set of feature vectors. Moreover, chaotic monarch butterfly optimization (CMBO) with deep wavelet neural network (DWNN) model is applied for classification purposes. The CMBO algorithm is utilized to optimally tune the parameters involved in the DWNN model in such a way that the classification performance gets improved. The performance validation of the proposed model takes place on benchmark MICC-F220, MICC-F2000, MICC-F600 datasets. A wide range of comparative analyses is performed and the results ensured the better performance of the IDL-CMIFD technique in terms of different evaluation parameters.
MCDM-EFS: A novel ensemble feature selection method for software defect prediction using multi-criteria decision making

Software defect prediction models are used for predicting high risk software components. Feature selection has significant impact on the prediction performance of the software defect prediction models since redundant and unimportant features make the prediction model more difficult to learn. Ensemble feature selection has recently emerged as a new methodology for enhancing feature selection performance. This paper proposes a new multi-criteria-decision-making (MCDM) based ensemble feature selection (EFS) method. This new method is termed as MCDM-EFS. The proposed method, MCDM-EFS, first generates the decision matrix signifying the feature’s importance score with respect to various existing feature selection methods. Next, the decision matrix is used as the input to well-known MCDM method TOPSIS for assigning a final rank to each feature. The proposed approach is validated by an experimental study for predicting software defects using two classifiers K-nearest neighbor (KNN) and naïve bayes (NB) over five open-source datasets. The predictive performance of the proposed approach is compared with existing feature selection algorithms. Two evaluation metrics – nMCC and G-measure are used to compare predictive performance. The experimental results show that the MCDM-EFS significantly improves the predictive performance of software defect prediction models against other feature selection methods in terms of nMCC as well as G-measure.
Visual Explanations of Differentiable Greedy Model Predictions on the Influence Maximization Problem

Social networks have become important objects of study in recent years. Social media marketing has, for example, greatly benefited from the vast literature developed in the past two decades. The study of social networks has taken advantage of recent advances in machine learning to process these immense amounts of data. Automatic emotional labeling of content on social media has, for example, been made possible by the recent progress in natural language processing. In this work, we are interested in the influence maximization problem, which consists of finding the most influential nodes in the social network. The problem is classically carried out using classical performance metrics such as accuracy or recall, which is not the end goal of the influence maximization problem. Our work presents an end-to-end learning model, SGREEDYNN, for the selection of the most influential nodes in a social network, given a history of information diffusion. In addition, this work proposes data visualization techniques to interpret the augmenting performances of our method compared to classical training. The results of this method are confirmed by visualizing the final influence of the selected nodes on network instances with edge bundling techniques. Edge bundling is a visual aggregation technique that makes patterns emerge. It has been shown to be an interesting asset for decision-making. By using edge bundling, we observe that our method chooses more diverse and high-degree nodes compared to the classical training.
DFLOWFS: Deep Fast-Learning Optimal Weight Fuzzy System with bottom-up hierarchical structure for high-dimensional data regression

As a model for reasoning and decision-making based on fuzzy rules, fuzzy systems have high interpretability. However, when the data dimension increases, the fuzzy system will face the problem of “rule explosion”, making it difficult to learn and predict effectively. In this paper, the fuzzy system trained by the FLOWFS (Fast-Learning with Optimal Weights for Fuzzy Systems) algorithm is used as sub-module in the deep fuzzy system, and the deep fuzzy system DFLOWFS (Deep FLOWFS) is constructed from the bottom-up hierarchical structure as the following three steps. 1) The FLOWFS algorithm assigns weight attributions to each fuzzy rule, and the rule weights are trained by the least square method with regularization terms to shorten training time and improve accuracy. 2) Three strategies of dividing high-dimensional inputs into multiple low-dimensional inputs are proposed as sequential division, random division and correlation division. Then, it is verified by experiments that the correlation division has the best performance. 3) The sub-module discarding method is proposed to discard the sub-modules with poor performance to have a maximum improvement of 13.8% compared to the DFLOWFS without using the sub-module discarding method. Then, the optimized DFLOWFS is verified and compared with the other three classic regression models on the three UCI datasets. Experiments show that with the increase of the data dimension, DFLOWFS not only have good interpretability but also have good accuracy. Furthermore, DFLOWFS performs best among all models in comprehensive scores, with good learning ability and generalization ability. Therefore, the proposed strategies with hierarchical structure for optimal shallow fuzzy systems are effective, which give a new insight for fuzzy system research.
Artificial Intelligence in Education

Despite its controversial nature, machine translation (MT) has been increasingly integrated into learning in the past decade. This controversy arises from two different beliefs. While some believe that MT negatively impacts students' language proficiency, others argue that it allows students to stay abreast of technological advancements. Despite the numerous risks associated with the unstoppable development and irresistible use of MT, it is imperative to explore appropriate integration methods instead of outrightly banning its use in learning the translation practice. Consequently, this article examines existing research on methods of using MT in classroom learning and highlights its strengths and limitations. The article explores pedagogical solutions to harness the capabilities of MT and proposes a novel approach for the practical and efficient utilization of GNMT in translation-learning classroom. The findings propose a novel strategy for optimizing the efficacy of GNMT tools in the context of classroom learning. Also, they emphasize the importance of integrating MT tools in classroom and to the curriculum design as a fast-developed technology tool.
The Utilization and Obstacles of Online Learning Applications

The use of online learning applications is constrained by the abilities of teachers and the lack of facilities and infrastructure that affect student learning outcomes. This study aims to analyze the use of applications during the online learning process and the obstacles encountered during that process. The approach used is descriptive qualitative. The subjects of this study were the teachers of grades 4, 5, and 6. Observations, interviews, and documentation are the data collection techniques used in this study. The data obtained were then analyzed qualitatively by means of data reduction, data presentation, and drawing conclusions. The results of this study show that, first, the online learning process uses the WhatsApp application for communication, attendance, and opening. YouTube application to explain material if learning is carried out non-face-to-face (asynchronous). Google Meet and Zoom applications for virtual (synchronously) face-to-face learning. The Google Classroom, Google Form, and Quiz applications are used for assessment when evaluating learning. Second, the obstacles to using the application are low learning concentration, low learning outcomes, low self-confidence, and a lack of facilities for using online applications. The results of this study are useful for overcoming problems regarding the use of online learning applications.
The Use of ICT In English Learning: Students’ Perceptions and Expectations

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Since the technology development educators should consider this to be a value for educational purposes. This study attempts to investigate the students’ perceptions and students’ expectations on the use of ICT in English learning. The writer sees a need to carry out this study as it provides a platform to discover pedagogical implications that would benefit the Y-generation in terms of using ICTs as media in their English learning. The respondents of this research comprise 20-second year students completing their bachelor’s degree in English literature faculty, The State Institute for Islamic Studies (IAIN) Sultan Thaha Syaifuddin Jambi, Sumatra, Indonesia. The students’ perceptions and students’ expectations were measured through a questionnaire. The questionnaire aims to find out about college students’ perception and expectations of computer technology use in English learning. There are three parts of questionnaire; Part one found present English learning and teaching situation in their institute and divided into computer facilities in their institutes, present their English learning situation, and students use of ICT in their daily life. Part two found students’ perceptions of the use of ICT in English learning, and Part three found students’ expectations of the use of ICT in English learning. The findings showed that respondents agree that ICT is useful for English learning especially for the item ‘computer can serve as tools for learning purposes’. And respondents generally agree to the statements which lead to the point that ICT can be expected to help respondents’ in English learning especially for the item ‘I need teachers/experienced people to teach me how to use online learning resources’, ‘I hope computers can be used to provide more interactive activities for students’, and ‘I hope the use of ICT will help me to learn English more efficiently’. The results of this study may be beneficial to EFL educators in incorporating ICT into their teaching repertoire.&#x0D; &#x0D; &#x0D; &#x0D;