Reviewerly/exHarmony huggingface dataset:
Floquet analysis of frequency collisions

Implementation of high-fidelity gate operations on integrated-qubit systems is vital for fault-tolerant quantum computation. Qubit-frequency allocation is an essential part of improving control fidelity. A metric for qubit-frequency allocation, frequency collision, has been proposed on simple systems of only a few qubits driven by a monomodal microwave drive. However, frequency allocation for quantum processors for more advanced purposes, such as quantum error correction, needs further investigation. In this study, we propose a Floquet analysis of frequency collisions. The key to our proposed method is a reinterpretation of frequency collisions as an unintended degeneracy of Floquet states, which allows a collision analysis on more complex systems with many qubits driven by multimodal microwave drives. Although the Floquet state is defined in an infinite-dimensional Hilbert space, we develop algorithms, based on operation perturbation theory, to truncate the Hilbert space down to the optimal computational complexity. In particular, we show that the computational complexity of the collision analysis for a sparse qubit lattice is linear with the number of qubits. Finally, we demonstrate our proposed method on cross-resonance-based experimental protocols. We first study the cross-resonance gate in an isolated three-qubit system, where the effectiveness of our method is verified by comparing it with previous studies. We next consider the more complex problem of syndrome extraction in the heavy-hexagon code [Christopher et al., Phys. Rev. X 10, 011022 (2020). doi:10.1103/PhysRevX.10.011022]. Our proposed method advances our understanding of quantum control for quantum processors and contributes to their improved design and control.
Study on cluster centers optimization of max-min distance k-means clustering algorithm based on inter-cluster separation measure

Clustering is an essential unsupervised technique when category information is not available. Although K-means and Max-min distance K-means clustering algorithms are widely used, they have some disadvantages such as dependence on the initial centers, sensitivity to outliers caused by using only distance as the clustering criterion. To overcome the problems, this paper proposes SMM-K-means algorithm which overcomes the dependence on the initial cluster centers and the initial number of clusters and the sensitivity to the outliers. First, the initial value K of the optimal cluster number is determined by the elbow method, and K-means is used for initial clustering. A new inter-cluster separation measure is then constructed based on the idea of q-nearest neighbors, which is constructed by comprehensive considering the separation between clusters and the distribution compactness of clusters themselves. Finally, the two sample points with highest degree of separation are brought into Max-min distance K-means algorithm as new initial centers for clustering. The definite determining method of cluster centers eliminates the complicated iterative calculation, and the construction of inter-cluster separation measure overcomes the sensitivity of clustering results to noise points and isolated points, and has good applicability and generalization. In addition, this algorithm is not limited by the shape and size of the clusters and has better flexibility. The experimental results show that the SMM-K-means algorithm has higher CH values, resulting in a better clustering effect and stability.
Profiling with trust: system monitoring from trusted execution environments

Abstract Large-scale attacks on IoT and edge computing devices pose a significant threat. As a prominent example, Mirai is an IoT botnet with 600,000 infected devices around the globe, capable of conducting effective and targeted DDoS attacks on (critical) infrastructure. Driven by the substantial impacts of attacks, manufacturers and system integrators propose Trusted Execution Environments (TEEs) that have gained significant importance recently. TEEs offer an execution environment to run small portions of code isolated from the rest of the system, even if the operating system is compromised. In this publication, we examine TEEs in the context of system monitoring and introduce the Trusted Monitor (TM), a novel anomaly detection system that runs within a TEE. The TM continuously profiles the system using hardware performance counters and utilizes an application-specific machine-learning model for anomaly detection. In our evaluation, we demonstrate that the TM accurately classifies 86% of 183 tested workloads, with an overhead of less than 2%. Notably, we show that a real-world kernel-level rootkit has observable effects on performance counters, allowing the TM to detect it. Major parts of the TM are implemented in the Rust programming language, eliminating common security-critical programming errors.
Fundamental limits on quantum cloning from the no-signaling principle

The no-cloning theorem is a cornerstone of quantum cryptography. Here we generalize and rederive in a unified framework various upper bounds on the maximum achievable fidelity of probabilistic and deterministic cloning machines. Building on ideas by Gisin [Phys. Lett. A 242, 1 (1998)], our result follows from the fact that remote state preparation is possible and the no-signaling principle holds. We apply our general theorem to several subsets of states that are of interest in quantum cryptography.
A planar tracking strategy based on multiple-interpretable improved PPO algorithm with few-shot technique

Abstract Facing to a planar tracking problem, a multiple-interpretable improved Proximal Policy Optimization (PPO) algorithm with few-shot technique is proposed, namely F-GBQ-PPO. Compared with the normal PPO, the main improvements of F-GBQ-PPO are to increase the interpretability, and reduce the consumption for real interaction samples. Considering to increase incomprehensibility of a tracking policy, three levels of interpretabilities has been studied, including the perceptual, logical and mathematical interpretabilities. Detailly speaking, it is realized through introducing a guided policy based on Apollonius circle, a hybrid exploration policy based on biological motions, and the update of external parameters based on quantum genetic algorithm. Besides, to deal with the potential lack of real interaction samples in real applications, a few-shot technique is contained in the algorithm, which mainly generate fake samples through a multi-dimension Gaussian process. By mixing fake samples with real ones in a certain proportion, the demand for real samples can be reduced.
Entwicklung eines neuen Ansatzes für eine digitale, kartographische Fels- und Reliefdarstellung

Abstract This paper presents a new approach to digital rock and relief representation. A prerequisite for the method was its applicability for different scales and thus different resolutions of the underlying terrain models. A further requirement was its suitability for different rock types. It must also take into account the rock surfaces that have become ice-free due to glacier retreat and be easy to integrate into the map in the event of an update. The relief representation follows the principle that the terrain should be reproduced as naturally as possible to provide the map user with the best possible orientation in the terrain. The approach requires a very good knowledge of the terrain as well as a certain artistic talent to achieve an appealing, natural color scheme. Through the process of color manipulation from the four-color set, it is also possible, in addition to the summer representation from the data collected with remote sensing methods, to generate winter representations for e.g. ski tourers. The use of established, automatic procedures considerably reduces the workload for the cartographer and also enables simple integration of corrections and changes to the landscape, e.g. due to glacial retreat and landslides, and can be applied to large map series due to its reproducibility.
Deep photonic network platform enabling arbitrary and broadband optical functionality

Expanding applications in optical communications, computing, and sensing continue to drive the need for high-performance integrated photonic components. Designing these on-chip systems with arbitrary functionality requires beyond what is possible with physical intuition, for which machine learning-based methods have recently become popular. However, computational demands for physically accurate device simulations present critical challenges, significantly limiting scalability and design flexibility of these methods. Here, we present a highly-scalable, physics-informed design platform for on-chip optical systems with arbitrary functionality, based on deep photonic networks of custom-designed Mach-Zehnder interferometers. Leveraging this platform, we demonstrate ultra-broadband power splitters and a spectral duplexer, each designed within two minutes. The devices exhibit state-of-the-art experimental performance with insertion losses below 0.66 dB, and 1-dB bandwidths exceeding 120 nm. This platform provides a tractable path towards systematic, large-scale photonic system design, enabling custom power, phase, and dispersion profiles for high-throughput communications, quantum information processing, and medical/biological sensing applications.
Deep Learning based Effective Watermarking Technique for IoT Systems Signal Authentication

In order to identify cyber-attacks, this research suggests a special watermarking technique for dynamic IoT System signal validation. IoT Systems (IoTSs) can extract a group of randomly generated characteristics from their produced signal and then periodically watermark these attributes into the transmission owing to the proposed efficient watermarking technique. Using dynamic watermarking for IoT signal authentication, a potent deep learning technique is used to detect cyber-attacks. Based on an LSTM structure, the proposed learning system enables IoT devices to extract a set of random features from the signal they release, hence enabling dynamic watermarking of the signal.
Rethinking Conversation Styles of Chatbots from the Customer Perspective: Relationships between Conversation Styles of Chatbots, Chatbot Acceptance, and Perceived Tie Strength and Perceived Risk

Grounded in the Stereotype Content Model, Risk Perception Theory, Technology Acceptance Model, and Relational Embeddedness Theory, this research delves into the relationship between chatbot conversation styles, customer risk, and the mediating role of chatbot acceptance and tie strength in online shopping. A 2 (warm vs. cold) * 2 (competent vs. incompetent) between-subjects experiment is conducted on 320 participants and the results obtained from two-way ANOVA and PROCESS macro revealed that: (a) customer-perceived risk decreases with conversation warmth rather than conversation competence; (b) customer acceptance of chatbots improves with conversation competence rather than conversation warmth, while not acting as an intermediary factor between the conversation styles and customer-perceived risk; (c) customer perceived tie strength increases with both conversation warmth and conversation competence. The findings contribute to the existing literature about the impact of chatbot anthropomorphism on customer cognitive processes and offer executives insights into the design of customer-friendly chatbots.
Geology, fluid inclusions, mineral and (S-O) isotope chemistry of the Badran orogenic Au deposit, Yana-Kolyma belt, eastern Siberia: implications for ore genesis

The Badran orogenic gold deposit is located in the Yana-Kolyma belt, Eastern Siberia; it has proven reserves of ∼9.3 t of gold and an average grade of 7.8 ppm. The total gold production at the Badran deposit since 1984 amounts to ∼34 t. Despite many years of study, the origin of the gold deposits of the Yana-Kolyma metallogenic belt, one of the world’s largest belts, and the Badran deposit is controversial. Synthesis of regional geology and geology of the Badran deposit, fluid inclusion analysis, mineral and (S-O) isotope chemistry defines the genetic model, origin of fluids, and source of metals in the evolution of the ore-forming system, equivalent to other orogenic gold deposits on the margin of the Siberian craton. The deposit is localized in the Upper Triassic clastic rocks and is controlled by the NW-trending thrust. Polyphase mineralization occurs as disseminated arsenian pyrite and arsenopyrite ores with invisible gold, quartz veins with native gold and Fe, Pb, Zn, Cu sulfides and sulfosalts of orogenic type, and locally post-ore Ag, Sb-bearing minerals and Hg epithermal features. The quartz veins with native gold were formed from low-medium saline (1.5–10 wt% NaCl eq.) aqueous-carbonic fluids boiling at temperatures of 290°C to 210 °C and pressures of 300–250 to 125 bar. The δ 34 S values of pyrite and arsenopyrite vary from −1.1‰ to +2.4‰, with an average of +0.4‰; the δ 18 О of quartz from +15.1‰ to +17.5‰ at constant δ 18 О H2O about +7.5‰ (±1.0‰). High contents of As (up to 2.4 wt%) and Co/Ni ∼ from 0.3 to 9.9 in pyrite of proximal alteration are typical for hydrothermal systems. The results obtained confirm that the ore-forming fluids did not have a single origin, but were formed from a mixture of subcontinental lithospheric mantle and metamorphic sources. The subcrustal lithospheric mantle was fertilized in the time preceding mineralization (Late Jurassic) and was derived directly from the down-going subduction slab and overlying sediment wedge at the closure of the Oymyakon Ocean.
Consumer’s acceptance of retail service robots: mediating role of pleasure and arousal

This study explores the influence of consumer emotions, specifically pleasure and arousal, on the intention to use retail service robots. Through a survey of 323 people using an online snowball sampling technique and analysis via the PLS-SEM method, it finds that subjective norms, technological self-efficacy, habitual behavior, and attitudes significantly affect users’ intentions. The research notably reveals that pleasure plays a more crucial role than arousal in mediating how consumers’ perceptions impact their willingness to interact with these robots. By integrating the Theory of Reasoned Action, self-efficacy theory, and the PAD model (pleasure, arousal, dominance), the study enhances the understanding of human-robot interaction. It provides actionable insights for businesses, suggesting that emphasizing emotional aspects, particularly pleasure, can be a strategic approach to encourage the adoption of service robots. These insights are valuable for designing marketing strategies and service experiences that effectively increase consumer engagement with robotic technology. Top of Form.
The Implementation of Canva Through Poster Creation on Fourth Graders

&lt;p&gt;&lt;em&gt;This community service program introduced Canva, a graphic design platform, to elementary school students as part of the University of Al-Azhar Indonesia’s Gerakan Nasional Revolusi Mental, under the theme of “Digital Literacy and Ethics”. The program was carried out at Al-Fityan School Tangerang on August 21, 2023. Students, in 4&lt;sup&gt;th&lt;/sup&gt; grade, were trained to create a poster using Canva. They were asked to choose one of their five favorite things: mom, dad, hobby, food, or animal. The results showed that the popular choice was between “hobby” and “animal” while the least popular was “dad”. All 28 students produced their own posters and even six of them produced two posters. In this regard, the training of Canva seemed to be effective. There should be a continuation of training using the similar theme to Al-Fityan students for the future program given students’ progress and enthusiasm.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt;&lt;strong&gt; –&lt;/strong&gt; &lt;em&gt;Canva, Gerakan Nasional Revolusi Mental, Poster Creation, Fourth Graders, Digital Literacy&lt;/em&gt;&lt;/p&gt;
Coordinated ifs and theories of conditionals

Abstract This paper concerns the semantics of coordinated if -clauses, as in (1)-(2). It is argued that the meanings of such sentences are explained straightforwardly on theories of conditionals that tie their non- monotonic behaviour to the if -clause itself (e.g. Schlenker 2004, but not theories that tie it to a (covert) modal operator (e.g. Kratzer 1981; 1991). Coordinated if -clauses are revealing of the fine-grained compositional semantics of conditionals.
Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models

Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods have been proposed to remove personal identifiers, with deep learning-based models achieving better performance. However, manual annotation of training data is time-consuming and expensive. The aim of this study was to develop an automatic de-identification pipeline for all kinds of clinical documents based on a distant supervised method to significantly reduce the cost of manual annotations and to facilitate the transfer of the de-identification pipeline to other clinical centers.We proposed an automated annotation process for French clinical de-identification, exploiting data from the eHOP clinical data warehouse (CDW) of the CHU de Rennes and national knowledge bases, as well as other features. In addition, this paper proposes an assisted data annotation solution using the Prodigy annotation tool. This approach aims to reduce the cost required to create a reference corpus for the evaluation of state-of-the-art NER models. Finally, we evaluated and compared the effectiveness of different NER methods.A French de-identification dataset was developed in this work, based on EHRs provided by the eHOP CDW at Rennes University Hospital, France. The dataset was rich in terms of personal information, and the distribution of entities was quite similar in the training and test datasets. We evaluated a Bi-LSTM + CRF sequence labeling architecture, combined with Flair + FastText word embeddings, on a test set of manually annotated clinical reports. The model outperformed the other tested models with a significant F1 score of 96,96%, demonstrating the effectiveness of our automatic approach for deidentifying sensitive information.This study provides an automatic de-identification pipeline for clinical notes, which can facilitate the reuse of EHRs for secondary purposes such as clinical research. Our study highlights the importance of using advanced NLP techniques for effective de-identification, as well as the need for innovative solutions such as distant supervision to overcome the challenge of limited annotated data in the medical domain.
Pengembangan media pembelajaran berbasis android menggunakan articulate storyline 3 materi program linear di sekolah menengah atas

The purpose of this research to create learning media for Android using Articulate Storyline 3 (AS3) as well as identifying validity and practicality of this Android-based learning tool developed with AS3. This research uses the ADDIE model, encompassing five stages: analysis, design, development, implementation, and evaluation. The participants in this study consist of two subject matter experts, two media experts, and 32 respondents from the ninth-grade students of SMA Negeri 3 Pontianak. The focus of the research is on the Android-based linear program learning media developed during the study. The data collection instrument is a questionnaire with descriptive statistical analysis techniques. The findings indicate that the validation analysis, based on assessments from material and media experts, yields an average score of 4.18, categorizing as 'good.' Furthermore, the practicality analysis, gauged through student responses, averages at 4.33, indicating a 'very good' category. Consequently, the Android based learning media employing AS3 is deemed both valid and practical for utilization.
Mind on the battlefield: what can cognitive science add to the military lessons-learned process?

In this paper, we demonstrate the advantages of combining the detailed military history of actual battles with contemporary decision-making cognitive theories. Specifically, we analyse two battle decisions made in Israeli-Arab wars, and interpret, through a cognitive lens, some of the decisions that were subsequently deemed “incomprehensible” in the civilian and military literature. This perspective permits analysis of multiple processes – such as risk evaluation, mental completion of unknown information, and estimation of enemy response. Moreover, it predicts that even highly experienced commanders are prone to the effect of cognitive biases, and may therefore make bad decisions with disastrous consequences.
A Hybrid Model Based on Convolutional Neural Network and Long Short-Term Memory for Multi-label Text Classification

Abstract Multi-label text classification (MLTC) is a popular method for organizing electronic documents, which is crucial for accessing and processing data. As the number of classes increases, learning multi-label data will be challenging. The number of possible states for various labels increases exponentially, and learning algorithms in single-label data cannot be used to solve these problems. In the meantime, using single-label data algorithms could be very time-consuming. In MLTC, complexity costs should be reduced. Deep-learning neural networks that can learn intricate patterns are used in many real-world problems because of their high power and accuracy. This paper proposed a hybridization of the long short-term memory (LSTM) neural network and the convolutional neural network (CNN) method for MLTC. The proposed model uses LSTM to enhance CNN to improve the proposed model’s accuracy. Also, the competitive search algorithm (CSA) is used to improve the LSTM hyperparameters. The LSTM hyperparameters play an important role in increasing the detection accuracy. The CSA algorithm finds the best values for the hyperparameters by searching the problem space. It was tested on four different datasets of multi-label texts: Reuters-21578, RCV1-v2, EUR-Lex, and Bookmarks. The result showed that the proposed model performed better than CNN and LSTM-CSA in terms of accuracy percentage and that it has improved by an average of more than 10%. Also, the results show that the LSTM-CSA model has higher detection accuracy compared to LSTM—Gradient-based optimizer (GBO) and LSTM—whale optimization algorithm (WOA).