Reviewerly/exHarmony huggingface dataset:
Adaptive tracking of non-ellipsoidal extended target with varying number of sub-objects based on variational Bayesian

High-resolution sensors make it less difficult to detect the true shape of an extended target, and the method of using a single ellipsoid to approximate the extended state of the target is no longer applicable. We can use multiple ellipsoids to approximate the shape of non-ellipsoidal extended targets, but the method of fixing number of sub-objects again fails due to changes in the target shape. In this work, we propose a novel variational Bayesian adaptive filter for non-ellipsoidal extended target tracking (NETT) with a varying number of sub-objects called VN-NETT-VBAF. The proposed filter is used to estimate the motion, extended states, and mixture probabilities of sub-objects describing a non-ellipsoidal extended target. First, the framework of VN-NETT-VBAF is established based on the spawning and combination of sub-objects. Second, the inverse Gamma (IG) distribution is used to model the unknown measurement noise covariance. Based on the variational Bayesian theory, the joint posterior probability density function (PDF) of the system parameters is iteratively updated to achieve the joint estimation of the extended target state and the measurement noise covariance. Finally, an adaptive forgetting factor is designed to update the noise covariance to further improve the robustness of the algorithm to abrupt noise. The simulation results show that the algorithm in this paper can reasonably change the number of sub-objects to adapt to the change of the target extended state, and effectively deal with the abrupt noise to obtain higher filtering accuracy.
Artificial Intelligence–Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens

Digital pathology adoption allows for applying computational algorithms to routine pathology tasks. Our study aimed to develop a clinical-grade artificial intelligence (AI) tool for precise multiclass tissue segmentation in colorectal specimens (resections and biopsies) and clinically validate the tool for tumor detection in biopsy specimens. The training data set included 241 precisely manually annotated whole-slide images (WSIs) from multiple institutions. The algorithm was trained for semantic segmentation of 11 tissue classes with an additional module for biopsy WSI classification. Six case cohorts from 5 pathology departments (4 countries) were used for formal and clinical validation, digitized by 4 different scanning systems. The developed algorithm showed high precision of segmentation of different tissue classes in colorectal specimens with composite multiclass Dice score of up to 0.895 and pixel-wise tumor detection specificity and sensitivity of up to 0.958 and 0.987, respectively. In the clinical validation study on multiple external cohorts, the AI tool reached sensitivity of 1.0 and specificity of up to 0.969 for tumor detection in biopsy WSI. The AI tool analyzes most biopsy cases in less than 1 minute, allowing effective integration into clinical routine. We developed and extensively validated a highly accurate, clinical-grade tool for assistive diagnostic processing of colorectal specimens. This tool allows for quantitative deciphering of colorectal cancer tissue for development of prognostic and predictive biomarkers and personalization of oncologic care. This study is a foundation for a SemiCOL computational challenge. We open-source multiple manually annotated and weakly labeled test data sets, representing a significant contribution to the colorectal cancer computational pathology field.
CViTF-Net: A Convolutional and Visual Transformer Fusion Network for Small Ship Target Detection in Synthetic Aperture Radar Images

Detecting small ship targets in large-scale synthetic aperture radar (SAR) images with complex backgrounds is challenging. This difficulty arises due to indistinct visual features and noise interference. To address these issues, we propose a novel two-stage detector, namely a convolutional and visual transformer fusion network (CViTF-Net), and enhance its detection performance through three innovative modules. Firstly, we designed a pyramid structured CViT backbone. This design leverages convolutional blocks to extract low-level and local features, while utilizing transformer blocks to capture inter-object dependencies over larger image regions. As a result, the CViT backbone adeptly integrates local and global information to bolster the feature representation capacity of targets. Subsequently, we proposed the Gaussian prior discrepancy (GPD) assigner. This assigner employs the discrepancy of Gaussian distributions in two dimensions to assess the degree of matching between priors and ground truth values, thus refining the discriminative criteria for positive and negative samples. Lastly, we designed the level synchronized attention mechanism (LSAM). This mechanism simultaneously considers information from multiple layers in region of interest (RoI) feature maps, and adaptively adjusts the weights of diverse regions within the final RoI. As a result, it enhances the capability to capture both target details and contextual information. We achieved the highest comprehensive evaluation results for the public LS-SSDD-v1.0 dataset, with an mAP of 79.7% and an F1 of 80.8%. In addition, the robustness of the CViTF-Net was validated using the public SSDD dataset. Visualization of the experimental results indicated that CViTF-Net can effectively enhance the detection performance for small ship targets in complex scenes.
Dynamic phases induced by two-level system defects on driven qubits

Recent experimental evidences point to two-level defects, located in the oxides and on the interfaces of the Josephson junctions, as the major constituents of decoherence in superconducting qubits. How these defects affect the qubit evolution with the presence of external driving is less well understood since the semiclassical qubit-field coupling renders the Jaynes–Cummings model for qubit-defect coupling undiagonalizable. We analyze the decoherence dynamics in the continuous coherent state space induced by the driving and solve the master equation endowed with an extra decay-cladded driving term via a Fokker–Planck equation. The solutions for diffusion propagators as Gaussian distributions show four distinct dynamic phases: four types of convergence paths to limit cycles of varying radius by the distribution mean, which are determined by the competing external driving and the defect decays. The qubit trajectory resulted from these solutions is a super-Poissonian over displaced Fock states, which reduces to a Gibbs state of effective temperature decided by the defect at zero driving limit. Furthermore, the Poincare map shows the dependence of the rate of convergence on the initial state. In other words, the qubit evolution can serve as an indicator of the defect coupling strength through the variation of the driving strength as a parameter.
A superconducting quantum processor architecture design method for improving performance and reducing frequency collisions

More physical qubits and qubit connections integrated on a superconducting quantum processor can improve the ability to execute quantum programs, but on the other hand, they might increase the probability of frequency collisions. Considering a performance and frequency collisions trade-off, it is a feasible method to optimize processor architecture aiming at running specific quantum programs. To this end, we propose a method for designing superconducting quantum processor architectures for the purpose of running specific quantum programs. Different from existing methods, our method is mainly based on graph theory to optimize processor architecture design. First, we convert the trade-off problem into the optimization of the distance between two points as well as the maximum degree in the processor architecture graph. Second, we consider the actual physical routing constraints and build a mathematical model for the optimization problem. Finally, we propose an automatic processor architecture design flow based on the mathematical model, which is implemented with an improved genetic algorithm. To show the effectiveness of our method, we selected sixteen quantum programs with different qubit numbers and different functions for comparison. Simulation results show that the architecture schemes of our method outperform IBM’s general-purpose square lattice architecture schemes. Compared to the general-purpose architecture schemes, the architecture schemes of our method have an average performance improvement of 15.61% and a minimum reduction of 21.33% in the probability of frequency collisions. Furthermore, in most of the selected quantum programs, our architecture schemes perform better than the eff-5-freq’s architecture schemes, with an average 6.58% improvement in performance and a minimum 6.45% reduction in the probability of frequency collisions. Therefore, our method can provide superconducting quantum processor architecture design with better performance and lower probability of frequency collisions for quantum programs.
Style-independent radical sequence learning for zero-shot recognition of Small Seal script

Small Seal script (Chinese called XiaoZhuan), as the earliest archaic form of standardized Chinese script, is the predecessor of modern Chinese characters. However, the Small Seal character recognition remains a challenging task, due to various un-/semi-structured pictographic glyphs and writing styles. This paper proposes a style-independent pictographic radical decomposition for the zero-shot recognition of Small Seal script, by taking advantage of the inherent consistency of pictographic representations between the Small Seal script and the Traditional Chinese script (Chinese called Fanti). Specifically, we design a feature-level collaboration framework of two tasks. One is the XiaoZhuan-to-Fanti translation task, which employs a generative adversarial network (GAN) based dual-learning mechanism to learn style-independent and consistent pictographic feature representations from different styles of Small Seal and their corresponding Traditional Chinese characters. The other is a transformer-based pictographic radical sequence learning from the pictographic feature representations. Experiments demonstrate that our model has satisfactory recognition ability to various styles of Small Seal scripts, especially for the zero-shot recognition of those with unknown glyphs and unseen styles. The code is available at https://github.com/windyz77/SmallSealRecon.
Multi-Keyword Searchable Identity-Based Proxy Re-Encryption from Lattices

To protect the privacy of cloud data, encryption before uploading provides a solution. However, searching for target data in ciphertext takes effort. Therefore, searchable encryption has become an important research topic. On the other hand, since the advancement of quantum computers will lead to the crisis of cracking traditional encryption algorithms, it is necessary to design encryption schemes that can resist quantum attacks. Therefore, we propose a multi-keyword searchable identity-based proxy re-encryption scheme from lattices. In addition to resisting quantum attacks, the proposed scheme uses several cryptographic techniques to improve encryption efficiency. First, identity-based encryption is used to reduce the computation and transmission costs caused by certificates. Second, the proposed scheme uses proxy re-encryption to achieve the purpose of outsourced computing, allowing the proxy server to reduce the computation and transmission costs of the users. Third, the proposed multi-keyword searchable encryption can provide AND and OR operators to increase the flexibility of searchability. Moreover, the access structure of the proposed scheme is not based on a linear secret sharing scheme (LSSS), avoiding the errors caused by an LSSS-based structure in decryption or search results. Finally, we also give formal security proof of the proposed scheme under the decisional learning with errors assumption.
An efficient and secure identity-based integrity auditing scheme for sensitive data with anti-replacement attack on multi-cloud storage

The rapid growth of cloud storage brings forth significant difficulties in ensuring the integrity of data owners' sensitive data. Multi-cloud storage systems, which motive to enhance trustworthiness, are vulnerable to rigorous replacement attacks. These insidious attacks involve malicious actors substituting legitimate data with counterfeit versions, leading to compromised integrity and fraudulent audit outcomes. We propose a novel identity-based data integrity auditing protocol tailored for multi-cloud storage environments to tackle this challenge. Our protocol leverages a Third-Party Auditor (TPA) to delegate data authenticity and auditing responsibilities, employing identity-based blind signatures with a robust data recovery scheme. By eliminating the need for cumbersome public key certificate management, our solution effectively protects data owners' sensitive data while ensuring confidentiality across multiple cloud providers, thereby mitigating the risk of replacement attacks. A thorough security examination shows the proposed system resists forging, replacement attacks, and data confidentiality violations. A rigorous performance experiment also reveals outstanding computational and communication efficiency that outperforms current methods in tag production, proof generation, and verification. Our protocol offers an extraordinary balance of security and efficiency, making it particularly useful for auditing patent-sensitive data in multi-cloud storage. Our proposal provides a timely and effective way to protect sensitive data and prevent attackers in multi-cloud storage settings at a time when replacement attacks pose a serious concern.
Differential Privacy-Based Spatial-Temporal Trajectory Clustering Scheme for LBSNs

Location privacy preserving for location-based social networks (LBSNs) has been attracting a great deal of attention. Existing location privacy protection methods are disadvantaged by issues such as information leakage and low data availability, which are no longer suitable for the current diverse and personalized location-based services. To address these issues, we propose a differential privacy-based spatial-temporal trajectory clustering (DP-STTC) scheme, which mainly transforms the existing location privacy protection mechanism into a spatial-temporal trajectory protection mechanism by adjusting the privacy parameters. Then, the trajectories were clustered to uncover users with similar trajectory characteristics. Finally, experiments were conducted on two real datasets. The experimental results show that our DP-STTC scheme can not only achieve better accuracy in trajectory clustering, but also protect user privacy.
Fast Generalized Sliding Sinusoidal Transforms

Discrete cosine and sine transforms closely approximate the Karhunen–Loeve transform for first-order Markov stationary signals with high and low correlation coefficients, respectively. Discrete sinusoidal transforms can be used in data compression, digital filtering, spectral analysis and pattern recognition. Short-time transforms based on discrete sinusoidal transforms are suitable for the adaptive processing and time–frequency analysis of quasi-stationary data. The generalized sliding discrete transform is a type of short-time transform, that is, a fixed-length windowed transform that slides over a signal with an arbitrary integer step. In this paper, eight fast algorithms for calculating various sliding sinusoidal transforms based on a generalized solution of a second-order linear nonhomogeneous difference equation and pruned discrete sine transforms are proposed. The performances of the algorithms in terms of computational complexity and execution time were compared with those of recursive sliding and fast discrete sinusoidal algorithms. The low complexity of the proposed algorithms resulted in significant time savings.
Photography Service Booking System

Development of booking information system is a computer technology development that will help and ease human works. This research is focused at the Aditya Production photo studio owned by researcher’s friend. Aditya Production is in need of a system that can help advertising and communicating with customer. Researcher hoping that this system could help Aditya Production to advertise their photography services and ease customer’s access to gain help from professional photo studio. Designing the booking system starts with making program design using activity diagram, administrator and customer navigation system. This system’s made using Google Web Design. Waterfall Research and descriptive method are used in this research
Higher Education Students’ Efforts to Overcome Internal Problems in the Academic Speaking Subject

This study aimed to find information about students’ internal problems in the academic speaking subject as well as students’ efforts to overcome the problems. The participants in this study were students who had passed the subject in the 2018 academic year in a private university in Bogor. This study was a qualitative research by using questionnaires and interviews as research instruments to explore information related to the two research questions: 1) what are the students’ internal problems in the academic speaking subject? and 2) what are the students' efforts to overcome the problems? This study discovered four kinds of internal problems, namely: the problems in expressing ideas, applying correct grammar patterns, speaking anxiety, and low motivation. This study also discovered students’ efforts to overcome the problems, including preparing the material to be discussed, practicing speaking up with friends, practicing pronouncing English words correctly, learning grammar, vocabulary and pronunciation from grammar books, dictionary, and English learning applications. The findings of this study are expected to be useful, not only for students to keep doing the efforts in improving their speaking skills, but also for teachers to support students’ efforts to overcome the internal problems in the academic speaking subject
RFID Based Employee Attendance System Design on CV. Sachio Express Sejahtera

CV. Sachio Express Sejahtera is a registered expedition company located in Surakarta. Attendance is the most crucial aspect for the company as it affects an employee's salary based on attendance data. Therefore, the presence of a real-time attendance system stored in a database is highly needed. The design of the employee attendance system utilizes the Rapid Application Development (RAD) method, which includes requirements planning, system design, development processes, and implementation. This method is employed to meet user needs and achieve desired outcomes.The result of the design for the employee attendance system is the utilization of RFID (Radio Frequency Identification) cards as a tool for attendance record. It is expected that this system will assist employees in recording their attendance and reduce any potential data manipulation or inaccuracies
Design and Development of Web-Based Information System Order Monitoring System

PT. Sanityasa Anjaya Daniswara is a technology company operating as a software house in Solo. Efficient monitoring of project progress is essential for improving efficiency. Thus, a monitoring system that allocates programmers, details work, and summarizes progress is urgently required. This study applies the RAD (Rapid Application Development) method to design a monitoring system that meets the company's needs. The result is a development progress monitoring system that provides an overview of work status, ensuring credibility of information, and enhancing productivity and success in software development projects
Teaching Strategy for Catholic Religious Education Teachers in Class XI Multiple Intelligence-Based Learning at Budi Murni 2 Medan Catholic Private Senior High School

The research aims to answer problems regarding implementation teaching strategy for catholic religious education teachers in class XI multiple intelligence-based learning at Budi Murni 2 Medan Catholic Private Senior High School.This study uses a descriptive qualitative approach. The subjects of this study are students of Class XI Budi Murni 2 Medan Catholic Private Senior High School. The informants specified in this study are 12 people consisting of catholic religious education students and teachers. The research is conducted during March and April 2023. Data analysis is carried out by reduction, display, and triangulation. The results of the study show that catholic religious education teachers at Budi Murni 2 Medan Catholic Private Senior High School have implemented appropriate teaching strategies in learning based on multiple intelligences. The suggestion of this research is that multiple intelligences can be used as a focus of teaching strategies for catholic religious education teachers in multiple intelligences-based learning at Budi Murni 2 Medan Catholic Private Senior High School
Dynamic Multi-Swarm Competitive Fireworks Algorithm for Global Optimization and Engineering Constraint Problems

As a novel intelligent algorithm, fireworks algorithm (FWA) is applied to deal with different types of optimization problems. Since FWA’s search processes are relatively simple, it is inefficient. In this paper, a dynamic multi-swarm competitive fireworks algorithm (DMCFWA) is developed to enhance the search capability of FWA. Firstly, based on the scaling coefficient updated by utilizing the fitness value of the optimal firework, the dynamic explosion amplitude strategy is proposed to improve the search capability of the best firework. Secondly, utilizing the location information of the fireworks, an improved search method is designed to enhance the local search capability of firework swarms. Thirdly, a multi-swarm independent selection technique and a restart operation are adopted to boost its abilities of global exploration and local exploitation. Finally, to reduce the computational cost of FWA, a new initialization method is used and a new model for calculating the spark number is embedded in DMCFWA. By adopting these strategies, DMCFWA easily implements and does well in exploitation and exploration. CEC2017 test suite and four engineering constraint problems are used to demonstrate the performance of DMCFWA. Experimental results show that DMCFWA performs more effectively and stably than its competitors.
EPT: Data Augmentation with Embedded Prompt Tuning for Low-Resource Named Entity Recognition

Data augmentation methods are often used to address data scarcity in natural language processing (NLP). However, token-label misalignment, which refers to situations where tokens are matched with incorrect entity labels in the augmented sentences, hinders the data augmentation methods from achieving high scores in token-level tasks like named entity recognition (NER). In this paper, we propose embedded prompt tuning (EPT) as a novel data augmentation approach to low-resource NER. To address the problem of token-label misalignment, we implicitly embed NER labels as prompt into the hidden layer of pre-trained language model, and therefore entity tokens masked can be predicted by the finetuned EPT. Hence, EPT can generate high-quality and high-diverse data with various entities, which improves performance of NER. As datasets of cross-domain NER are available, we also explore NER domain adaption with EPT. The experimental results show that EPT achieves substantial improvement over the baseline methods on low-resource NER tasks.