Reviewerly/exHarmony huggingface dataset:
Monopoly Edutainment Media and its Implementation Through the Course Review Horay Model in Learning to Write Indonesian

The low ability of students to write is due to teachers' need for more use of learning media during the learning process. So to overcome these problems, we need media to help the student learning process. This research aims to develop edutainment monopoly media and its implementation through the course review horay model. This research belongs to the type of development research which was developed using the ADDIE model. The ADDIE model comprises five research stages: analysis, design, development, implementation, and evaluation. The subjects of this study were two learning material experts and two learning media experts. The test subjects included two teachers, three individual students, and six small group test students. Methods of data collection using test and nontest methods. The data collection instrument used questionnaires and test questions. The techniques used in collecting data are descriptive quantitative analysis, qualitative, and inferential statistics. The study's results, namely assessments from content and learning media experts, obtained very good qualifications. The test results for the two teacher responses scored 91.7% (very good). The results of student responses were 91.94% (very good). The practicality test results scored 99.7% (very practical). The individual trial results were 93.6 (very good). The results of the small group trials were 91% (very good). The t-test results showed significant differences before and after using edutainment monopoly media and its implementation through the course review horay model. Based on these results, monopoly edutainment media and its implementation through the course review horay model can improve students' writing skills.
Thematic Structure of Students’ Anecdote Text

The thematic structure (theme-rheme) is the starting point of the message and develops. A text cannot be separated from a clause, making it a complete text. The purpose of this research is to determine the theme used in student anecdotal texts depending on the level of achievement determined by the score. This type of research is qualitative. These students come from class A and are taking the second semester. Of the 15 texts selected for the exam, they have been divided into three categories based on their level of attainment. Methods of data collection using documentation. This study uses the data analysis method proposed by Krippendorff. Based on the findings of this study, the themes that appeared the most were Topical themes, which appeared 139 times (63%), and Textual themes, which appeared 67 times (31%). And Interpersonal themes appeared 14 times (6%). At the moderate level, the theme that appeared the most was the topical theme which appeared 119 times, followed by the textual theme with 46 occurrences, and the interpersonal theme with 11. At the lowest level, the topical theme appeared the most, with 87 occurrences (67%), followed by textual themes (34 occurrences (26%), and interpersonal themes (9 occurrences (7%). At a high level, dual themes became the dominant theme appearing 85 times (61%), while simple themes appeared 54 times (39%). At the medium level, the simple theme is more dominant than the double theme at the low level, with 46 occurrences (53%), while the dual theme has 41 occurrences (46%).
Deep Imitation Learning for Optimal Trajectory Planning and Initial Condition Optimization for an Unstable Dynamic System

In this article, an innovative offline deep imitation learning algorithm for optimal trajectory planning is proposed. While many state‐of‐the‐art works achieved optimal trajectory planning, their systems were stable or quasistable, and their approaches rarely optimized the system's initial conditions (ICs). Here, a new unstable dynamic system task called “internal sliding object stabilization control” is proposed, modeled, and solved by deep imitation learning. Given the system's ICs, the neural networks (NNs) can imitate the iterative linear quadratic regulator (iLQR), generate optimal trajectories, and compute faster. A proportional–integral–derivative (PID) controller is used to track the unstable trajectories. Leveraging on the gradients of NNs, it can optimize the system's ICs, avoid obstacles stepwise, and ensure the worst bounds of NNs for safety. Subsequently, thorough simulations are conducted, including comparing the iLQR and PID controllers in the task, optimizing the system's different ICs by gradient descent, and finding the worst bound of the performance by gradient ascent. Results show that the proposed algorithm achieves considerably improved performance. Finally, experiments are conducted with a real manipulator to compare the proposed structure with the original iLQR. Results indicate that the proposed algorithm resembles the iLQR well. Program code and experiment results are in https://github.com/DanielYamChen/ISOSC.git.
Contextualized Sentiment Analysis using Large Language Models

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A secure multi‐authority attribute based encryption approach for robust smart grids

Abstract This paper presents EMBASS, an Enhanced Multi‐Authority Attribute‐Based Encryption System for Smart Grids. EMBASS supports outsourced decryption to devices with limited resources. It also uses a proxy to add fake values to the access policy in order to protect against malicious users. Additionally, a proxy user assistant is utilized to create an intermediate key for each fog node in the decryption process, and a chain decryption technique is used to further delegate the decryption process. The security properties and performance of the proposed system are analyzed, discussed and compared to state of the art approaches. EMBASS has been discussed for usage with smart grids, and experimentation shows that it yields promising results compared to state‐of‐the‐art approaches.
Text Finder Application for Android

A Text Finder, an android application that utilizes Optical Character Recognition (OCR) technology with the help of Google’s Cloud Vision API to extract text from images taken with the device camera or from existing images in the user’s phone. The extracted text can be saved to the device storage where all previous extracts can be easily accessed on a user-friendly interface. The application also features editing, deletion and sharing options for the extracted text. The user interface is user-friendly, making the application accessible to students, professional and organizations for a variety of purposes, including document scanning, data entry, and information retrieval. Manual extraction of text by typing or writing from images can be very time-consuming and can be prone to errors. This application is an efficient and simple solution for extracted texts and organizing important information from the photos. This paper describes the technical details of the OCR technology and Google’s ML Kit Text Recognition API used in the application, as well as the design, implementation and evaluation of the application in terms of performance and accuracy. The research also explores the key objectives and benefits of Text Finder, such as reducing the time and effort required and increasing the efficiency of document-based tasks.
Inequality on Appeal: The Intersection of Race and Gender in Patent Litigation

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MEV Makes Everyone Happy under Greedy Sequencing Rule

Trading through decentralized exchanges (DEXs) has become crucial in today's blockchain ecosystem, enabling users to swap tokens efficiently and automatically. However, the capacity of miners to strategically order transactions has led to exploitative practices (e.g., front-running attacks, sandwich attacks) and gain substantial Maximal Extractable Value (MEV) for their own advantage. To mitigate such manipulation, Ferreira and Parkes recently proposed a greedy sequencing rule where transactions within a block are executed in a back-and-forth manner around the starting price. Utilizing this sequencing rule restricts the feasibility of miners conducting sandwich attacks, consequently mitigating the MEV problem. However, no sequencing rule can prevent miners from obtaining risk-free profits. This paper systemically studies the computation of a miner's optimal strategy for maximizing MEV under the greedy sequencing rule, where the utility of miners is measured by the overall value of their token holdings. Our results unveil a dichotomy between the no trading fee scenario, where an optimal strategy can be computed in polynomial time, and the scenario with a constant fraction of trading fee, where finding an optimal strategy is NP-hard. The latter represents a significant challenge for miners seeking optimal MEV. Following the computation results, we further show a remarkable phenomenon: Miner's optimal MEV also benefits users. Precisely, in the scenarios without trading fees, when miners adopt the optimal strategy given by our algorithm, all users' transactions will be executed, and each user will receive as good profits as expected. This outcome provides further support for the study and design of sequencing rules in decentralized exchanges.
Anti‐occlusion person re‐identification via body topology information restoration and similarity evaluation

Abstract In real‐world scenarios, pedestrian images often suffer from occlusion, where certain body features become invisible, making it challenging for existing methods to accurately identify pedestrians with the same ID. Traditional approaches typically focus on matching only the visible body parts, which can lead to misalignment when the occlusion patterns vary. To address this issue and alleviate misalignment in occluded pedestrian images, the authors propose a novel framework called body topology information generation and matching. The framework consists of two main modules: the body topology information generation module and the body topology information matching module. The body topology information generation module employs an adaptive detection mechanism and capsule generative adversarial network to restore a holistic pedestrian image while preserving the body topology information. The body topology information matching module leverages the restored holistic image from body topology information generation to overcome spatial misalignment and utilises cosine distance as the similarity measure for matching. By combining the body topology information generation and body topology information matching modules, the authors achieve consistency in the body topology information features of pedestrian images, ranging from restoration to retrieval. Extensive experiments are conducted on both holistic person re‐identification datasets (Market‐1501, DukeMTMC‐ReID) and occluded person re‐identification datasets (Occluded‐DukeMTMC, Occluded‐ReID). The results demonstrate the superior performance of the authors proposed model, and visualisations of the generation and matching modules are provided to illustrate their effectiveness. Furthermore, an ablation study is conducted to validate the contributions of the proposed framework.
Unmasking Role-Play Attack Strategies in Exploiting Decentralized Finance (DeFi) Systems

The rapid growth and adoption of decentralized finance (DeFi) systems have been accompanied by various threats, notably those emerging from vulnerabilities in their intricate design. In our work, we introduce and define an attack strategy termed asRole-Play Attack, in which the attacker acts as multiple roles concurrently to exploit the DeFi system and cause substantial financial losses. We provide a formal definition of this strategy and demonstrate its potential impacts by revealing the total loss of \435.1M caused by 14 historical attacks with applying this pattern. Besides, we mathematically analyzed the attacks with top 2 losses and retrofitted the corresponding attack pattern by concrete execution, indicating that this strategy could increase the potential profit for original attacks by \3.34M (51.4%) and \3.76M (12.0%), respectively.
R-Pool and Settlement Markets for Recoverable ERC-20R Tokens

ERC-20R is a wrapper around ERC-20 that supports asset recovery within a limited time window after an asset is transferred. It is designed to reduce theft and losses on the blockchain by allowing a victim to recover their stolen or lost assets during the recovery window. When an honest recipient receives an ERC-20R asset, they must wait until the recovery windows elapses (say, 24 hours), before they can unwrap the asset back to its base ERC-20 form. We argue that many DeFi services will likely refuse to accept unsettled recoverable assets because they can interfere with their normal operations. Consequently, when Alice receives an ERC-20R token, she must wait 24 hours before she can use it with a DeFi service. But what if Alice is willing to pay a fee to exchange the wrapped token for an unwrapped ERC-20 token that can be used right away? In this paper we explore how to design a pool to exchange an unsettled ERC-20R asset for a base ERC-20 of the same asset. Designing such a pool raises several challenging questions and we present our solutions.
Intelligent vehicle localization and navigation based on intersection fingerprint roadmap (IRM) in underground parking lots

Abstract The localization and navigation of underground parking lots presents a ‘last mile’ problem for intelligent vehicles. To address the issues of poor localization and navigation efficiency caused by the lack of global navigation satellite system signals in this scenario, this study presents a low-cost and computationally efficient real-time localization and navigation algorithm. The algorithm begins by constructing a node fingerprint based on the characteristics of underground parking lot intersections and proposes an intersection fingerprint roadmap (IRM). To address the problem of difficult initial position computation during navigation, a node-level localization method based on scene matching and pose calculation is proposed and then combined with the IRM. Finally, the IRM is used to search for routes on the scene plan and perform path smoothing to obtain a globally feasible driving route. The algorithm proposed in this study can achieve fast vehicle localization and real-time navigation with low computational power, and the proposed localization method can be applied not only to underground parking lots but also to other outdoor scenes. The method is evaluated on datasets collected from different environments, and the experimental results show that the algorithm has an average localization error of approximately 20 cm and provides faster navigation speed and smoother navigation in comparison with other algorithms.
Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

Abstract In a Cloud Computing (CC) environment, users are charged based on on‐demand resource utilization and expected Quality of Service (QoS). Multi‐Objective‐based Task Scheduling (MOTS) problem formulated for achieving the expected QoS in a CC scenario is always NP‐complete. The majority of existing Task Scheduling (TS) algorithms are not efficient in determining global optimal solutions due to huge space offered by large‐scale problem instances and characteristics inherent in NP‐complete problems. In this article, an Adaptive Guided Differential Evolution‐based Slime Mould Algorithm (AGDESMA) is proposed for dealing with the problem of Multi‐Objective large‐scale TS optimization over Infrastructure‐as‐a‐Service (IaaS) CC environment. The proposed AGDESMA is formulated with the merits of Slime Mould Algorithm (SMA) and Adaptive Guided Differential Evolution (AGDE) for achieving a proper balance amid exploitation and exploration phases and preventing the solutions from getting trapped into minimum local regions. It specifically adopts the method of AGDE for preventing premature convergence with maximized diversity of population and local search of swarm agents. The experimental validation of the proposed AGDESMA is conducted using CloudSim based on the synthesized and standard workload traces with larger instances up to a maximum of 5000. The results confirm the predominance of the proposed AGDESMA scheme over benchmarked schemes based on computational overhead and trade‐off between makespan and financial cost.
Finite-size security proof of binary-modulation continuous-variable quantum key distribution using only heterodyne measurement

Abstract Continuous-variable quantum key distribution (CV-QKD) has many practical advantages including compatibility with current optical communication technology. Implementation using heterodyne measurements is particularly attractive since it eliminates the need for active phase locking of the remote pair of local oscillators, but the full security of CV QKD with discrete modulation was only proved for a protocol using homodyne measurements. Here we propose an all-heterodyne CV-QKD protocol with binary modulation and prove its security against general attacks in the ﬁnite-key regime. Although replacing a homodyne measurement with a heterodyne measurement would be naively expected to incur a 3-dB penalty in the rate-distance curve, our proof achieves a key rate with only a 1-dB penalty.&amp;#xD;
Quantifying channel coherence via the norm distance

Abstract Quantifying the number of resources contained in a physical object has been one of the core topics in the resource theory of coherence. In this paper, we introduce the dynamical coherence measures based on a class of norms in the classical channel setting. It is proved that it satisfies faithfulness, decreases monotonically under the maximally incoherent superchannels, and is convex. Moreover, we show that it satisfies subadditivity under both the composition and tensor product of channels. Especially, the diamond measure as a special case is discussed in detail, it can reduce to trace norm of coherence, satisfies amortization inequality, and can be calculated efficiently using a semidefinite program. In addition, we introduce the creation-coherent diamond measure and find that neither the detection coherence nor the creation coherence of a channel exceeds the coherence of the channel, which does not exceed the purity of the channel. Second, we introduce the corresponding dephasing measure, which is a dynamical coherence measure under the dephasing-covariant incoherent superchannels. Meanwhile, we also introduce the dephasing diamond measure as a special case. Third, we use the dephasing diamond measure to accurately calculate the coherence values of some important noisy channels such as amplitude damping channel, phase damping channel, and depolarizing channel, respectively, and give the sufficient and necessary conditions for an unital qubit channel with a parameter probability vector to be a coherent channel. Finally, the operational interpretation of our diamond measure in the binary channel discrimination task is investigated.
System-widely and fine-grained forward secure identity-based signature scheme

In an identity-based signature (IBS) system, each user uses individual information as his/her public key, and also holds a private key issued by a fully trusted key generation center (KGC) with a master secret key. This avoids the mandatory requirement of public key infrastructure, and thus motivates the wide deployment of IBS in various fields. In practical scenarios, the premise that IBS can provide the intended security guarantee is to remain secret keys unrevealed. That is, on the one hand, after a user’s secret key is leaked, then an adversary can use it to create correct signatures for any messages, which invalidates all signatures produced by this user him/herself. On the other hand, what is worse, after the master secret key is disclosed, then all users’ signatures become useless, since the adversary can produce any user’s private key with it. In this paper, to reduce the damage of secret key leakage in IBS system, we introduce system-widely and fine-grained forward-secure IBS (SWFG-fs-IBS). In this primitive, the master secret key is immediately punctured after issuing a secret key for a new user, such that it would fail to issue a secret key for the same user again. Similarly, we also perform puncture operations on each user’s private key after signing messages, which prevents the exposed private key from creating signatures for previously signed messages. Concretely, we first define the syntax and security notions of SWFG-fs-IBS, and then propose its concrete construction with provable security. The theoretical analysis indicates that our proposal provides stronger and more practical forward security than previous fs-IBS schemes. We also provide an implementation of our proposal, and present extensive experiments to demonstrate its feasibility.
Scalable quantum measurement error mitigation via conditional independence and transfer learning

Abstract Mitigating measurement errors in quantum systems without relying on quantum error correction is of critical importance for the practical development of quantum technology. Deep learning-based quantum measurement error mitigation (QMEM) has exhibited advantages over the linear inversion method due to its capability to correct non-linear noise. However, scalability remains a challenge for both methods. In this study, we propose a scalable QMEM method that leverages the conditional independence (CI) of distant qubits and incorporates transfer learning (TL) techniques. By leveraging the CI assumption, we achieve an exponential reduction in the size of neural networks used for error mitigation. This enhancement also offers the benefit of reducing the number of training data needed for the machine learning model to successfully converge. Additionally, incorporating TL provides a constant speedup. We validate the effectiveness of our approach through experiments conducted on IBM quantum devices with 7 and 13 qubits, demonstrating excellent error mitigation performance and highlighting the efficiency of our method.
Ambiguity in Language and Logic

Abstract This article gives an explanation of how recent results on ambiguity logics are relevant to the linguistic and philosophical theory of ambiguity. To this aim, some fundamental definitions and results are explained. We formulate and provide evidence for three main hypotheses: Firstly, ambiguity is not a vague notion. Secondly, in (explicit) reasoning with ambiguity, we always have to consider the parameter trust. Thirdly, ambiguous propositions exist, but they cannot have the same rights and properties as unambiguous propositions; rather they should be considered “second class”.
How has Virtual Reality Technology Been Used to Deliver Interventions That Support Mental Well-Being? A Systematic Literature Review

In this paper, the authors present findings from a systematic literature review that interrogates the degree to which virtual reality (VR) technology has been used to deliver interventions that support mental well-being. Following PRISMA guidelines, a total of n = 853 articles were returned. After removing duplicates and applying inclusion and exclusion criteria during abstract and title screening, a total of n = 12 articles remained. The full text of these 12 articles was analyzed and the results were summarized and synthesized. Based on our analysis, we assert that a better articulation of what constitutes VR in the field of mental well-being is needed. Further, findings suggest that research methodologies, study outcomes, target populations, etc., are remarkably heterogeneous, which could be explained by the complex nature of the concept of mental well-being. As a result, findings may lack reliability or generalizability due to disparate research approaches. Implications and directions for further research are provided.