Reviewerly/exHarmony huggingface dataset:
Steganography of Steganographic Networks

Steganography is a technique for covert communication between two parties. With the rapid development of deep neural networks (DNN), more and more steganographic networks are proposed recently, which are shown to be promising to achieve good performance. Unlike the traditional handcrafted steganographic tools, a steganographic network is relatively large in size. It raises concerns on how to covertly transmit the steganographic network in public channels, which is a crucial stage in the pipeline of steganography in real world applications. To address such an issue, we propose a novel scheme for steganography of steganographic networks in this paper. Unlike the existing steganographic schemes which focus on the subtle modification of the cover data to accommodate the secrets. We propose to disguise a steganographic network (termed as the secret DNN model) into a stego DNN model which performs an ordinary machine learning task (termed as the stego task). During the model disguising, we select and tune a subset of filters in the secret DNN model to preserve its function on the secret task, where the remaining filters are reactivated according to a partial optimization strategy to disguise the whole secret DNN model into a stego DNN model. The secret DNN model can be recovered from the stego DNN model when needed. Various experiments have been conducted to demonstrate the advantage of our proposed method for covert communication of steganographic networks as well as general DNN models.
Online Symbolic Regression with Informative Query

Symbolic regression, the task of extracting mathematical expressions from the observed data, plays a crucial role in scientific discovery. Despite the promising performance of existing methods, most of them conduct symbolic regression in an offline setting. That is, they treat the observed data points as given ones that are simply sampled from uniform distributions without exploring the expressive potential of data. However, for real-world scientific problems, the data used for symbolic regression are usually actively obtained by doing experiments, which is an online setting. Thus, how to obtain informative data that can facilitate the symbolic regression process is an important problem that remains challenging. In this paper, we propose QUOSR, a query-based framework for online symbolic regression that can automatically obtain informative data in an iterative manner. Specifically, at each step, QUOSR receives historical data points, generates new x, and then queries the symbolic expression to get the corresponding y, where the (x, y) serves as new data points. This process repeats until the maximum number of query steps is reached. To make the generated data points informative, we implement the framework with a neural network and train it by maximizing the mutual information between generated data points and the target expression. Through comprehensive experiments, we show that QUOSR can facilitate modern symbolic regression methods by generating informative data.
Few-Shot Composition Learning for Image Retrieval with Prompt Tuning

We study the problem of composition learning for image retrieval, for which we learn to retrieve target images with search queries in the form of a composition of a reference image and a modification text that describes desired modifications of the image. Existing models of composition learning for image retrieval are generally built with large-scale datasets, demanding extensive training samples, i.e., query-target pairs, as supervision, which restricts their application for the scenario of few-shot learning with only few query-target pairs available. Recently, prompt tuning with frozen pretrained language models has shown remarkable performance when the amount of training data is limited. Inspired by this, we propose a prompt tuning mechanism with the pretrained CLIP model for the task of few-shot composition learning for image retrieval. Specifically, we regard the representation of the reference image as a trainable visual prompt, prefixed to the embedding of the text sequence. One challenge is to efficiently train visual prompt with few-shot samples. To deal with this issue, we further propose a self-upervised auxiliary task via ensuring that the reference image can retrieve itself when no modification information is given from the text, which facilitates training for the visual prompt, while not requiring additional annotations for query-target pairs. Experiments on multiple benchmarks show that our proposed model can yield superior performance when trained with only few query-target pairs.
Improved path planning algorithm for mobile robots

Path planning is an NP-hard problem in road network environments. Considering that the existing path planning algorithms mainly have the problems of low smoothness and low search efficiency in generating paths in large-scale complex environments, an improved rapidly exploring random tree (RRT) algorithm is proposed in this paper. First, the grid method is applied to model the road network environment, and the RRT algorithm based on adjacency expansion is proposed to search the initial path. Then, the strategies of identifying paths and eliminating redundant paths are adopted, respectively, to further optimize the selected paths. Experimental results show that, compared with other path planning algorithms, our algorithm can achieve faster convergence speed, shorter search path, and better smoothness in a complex map of the environment.
Use of Decentralized-Learning Methods Applied to Healthcare: A Bibliometric Analysis

The use of health data in research is fundamental to improve health care, health systems and public health policies. However, due to its intrinsic data sensitivity, there are privacy and security concerns that must be addressed to comply with best practices recommendations and the legal framework. Decentralized-learning methods allow the training of algorithms across multiple locations without data sharing. The application of those methods to the medical field holds great potential due to the guarantee of data privacy compliance. In this study, we performed a bibliometric analysis to explore the intellectual structure of this new research field and to assess its publication and collaboration patterns. A total of 3023 unique documents published between 2013 and 2023 were retrieved from Scopus, from which 488 were included in this review. The most frequent publication source was the IEEE Journal of Biomedical and Health Informatics (n = 27). China was the country with the highest number of publications, followed by the USA. The top three authors were Dekker A (n = 14), Wang X (n = 13), Li X (n = 12). The most frequent keywords were “Federated learning” (n = 218), “Deep learning” (n = 62) and “Machine learning” (n = 52). This study provides an overall picture of the research literature regarding the application of decentralized-learning in healthcare, possibly setting ground for future collaborations.
Eliminating illumination influence via Gaussian-based local sensitive histogram

Eliminating the influence of illumination is one of the important preprocessing methods to improve the stability of image processing algorithms. Shengfeng He provided an effective method to eliminate the influence of illumination by using local sensitive histogram. This method considers that the farther the distance between two pixels in the image, the smaller the influence of illumination on each other, which is an exponential relationship. However, this assumption has very low sensitivity for pixels at different distances from the center point, which leads to feature loss in images after eliminating the influence of illumination. Therefore, this paper applies Gaussian distribution to redefine the local sensitivity histogram, which effectively improves the sensitivity difference of pixel distance. The experimental comparison shows that in the case of large differences in lighting conditions, the algorithm proposed in this paper can retain more image features and is more robust in eliminating the influence of illumination.
Can Multi-Agent Consensus Improve Quality Tradeoffs in Software Architecture Optimization?

When designing a software architecture to fulfill quality-attribute requirements, architects normally explore and assess alternative solutions leading to different quality-attribute tradeoffs. In this context, we developed the SQuAT framework to support automated multi-objective optimization in large design spaces. SQuAT provides a modular, multi-agent architecture in which each agent represents and optimizes a particular quality attribute. However, this search strategy has problems identifying tradeoffs that satisfy all the parties (or architects’ concerns), particularly when searching for many candidate solutions and evaluating them becomes computationally costly. This is actually a general challenge for architecture optimization tools. To deal with it, SQuAT features an agent negotiation protocol that seeks consensus based on the utility of solutions as judged by each agent. In this paper, we present a parameterized heuristic that enhances the integration between search and negotiation in SQuAT, and also report on an empirical evaluation with two case studies. The results show initial evidence that using negotiation is more effective than doing a pure search to identify solutions having a balanced utility across agents, and thus, offer alternative quality-attribute tradeoffs to the architect.
Penampilan Watak Tokoh dalam Cerkak “Didongakne Malah Nesu” Karya Djaid Belor

The purpose of writing this scientific paper is to fulfill lecture assignments in order to develop writing skills and describe a person's character based on interactions between characters in a short story literary work or it can also be called cerkak. The story comes from a Javanese-language magazine published in 2019. However, what will be discussed in this scientific paper is the one with the title Didongake Malah Nesu which was created by Djaid Belor. The method used in this research is qualitative. Research data in the form of cerkak texts sourced from Javanese-language magazines published in 2019 tells of the reality of a husband and wife as officials who were accidentally prayed for by foreigners. The theory used is literary psychology that focuses on the behavior of characters that clearly describe the character's character. The results of this study are to find out the inner conflict that occurs in each character, to know the psychological condition of the character and the psychology of the author through the plot of the short story. Each character has a different role and response to the events they face to make the story interesting. This behavior becomes a reference for knowing the character's character through conversations and interactions between characters.
Time-Dependent Vehicle Speed Variation Based Global Path Planning for Intelligent Connected Vehicles

Article Time-Dependent Vehicle Speed Variation Based Global Path Planning for Intelligent Connected Vehicles Sihao Chen 1,2, Zhenfeng Wang 1,2, Zhengbai Liu 3, Xianyi Yang 1,2, and Heng Wang 1,2,* 1 College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China 2 Henan Provincial Cold Chain Information and Equipment Laboratory for Logistics of Agricultural Products, Zhengzhou 450002, China 3 College of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen 518055, China * Correspondence: dawn.wangh@henau.edu.cn Received: 8 May 2023 Accepted: 29 May 2023 Published: 21 June 2023 Abstract: When an intelligent connected vehicle (ICV) autonomously completes an intelligent driving assignment, the decision planning layer needs to plan an optimal path from the starting location to the target location for the vehicle, which is referred to as global path planning (GPP) for the ICV. For the GPP of ICVs undertaking long-distance and multi-location driving assignments, a fixed open travelling salesman problem (TSP) was constructed in conjunction with travel time analysis. To better address this issue, a genetic annealing algorithm (GAA) was proposed, and corresponding simulations were conducted using genetic algorithm, ant colony algorithm, and GAA respectively. Based on the optimization processes and results, the GAA outperformed the traditional genetic algorithm and ant colony algorithm in tackling this issue. Therefore, the method proposed in this paper can be applied to the global path planning platform for intelligent networked vehicles.
On the Shortage of Resources in Study Rooms in the Era of Novel Coronavirus - the Solution of the Intelligent Management System Based on the Self-study Folding Table System

After the covid-19 pandemic since 2020, the number of people who need to learn to improve their competitiveness has increased significantly.The need for ample study seating is increasingly prominent.To this end, an intelligent management scheme based on the self-learning folding table system is proposed, and two sets of self-learning folding table systems are developed to solve the two problems existing in the commercial study room of the public library.The technical scheme of the utility model is as follows: the self-learning folding table is characterized in that it comprises a left box body and a right door body, and the upper part of the left box body and the right door body are connected with a folding pull plate.The box on the left is an openable bookcase with a folding table in the lower middle; the door on the right has casters at the bottom.
IMPROVING STUDENTS’ READING COMPREHENSION ABILITY BY USING MAKE A MATCH ASSISTED BY FLASHCARD ON THE VIII CLASS OF SMP IT MADANI IN ACADEMIC YEAR 2021/2022

This study deals with the students’ ability to improve reading comprehension by using make a match assisted by flashcard on the VIII class of SMP IT Madani Academic Year 2021/2022. Its objective is to find out whether the use make a match assisted by flashcard has a significance improve reading comprehension. Population used in this study of class VIII were 60 and samples used in this study were 30 students. The method used in this study is an experimental method with a one group as pretest and posttest design. This study was carried on using quantitave method. The data was analysed by using t-test. The researcher found the pretest mean = 48,33 , deviation standard = 3,79 and experienced an increase in the posttest mean = 86,67 deviation standart = 4,79. From this analysis, writer found that guided make a match assisted by flashcard has significane in teaching reading comprehension. It can be proved by the result of the analysis showed that “” observed about 34,54 was higher than “ttable” about 2,045 with the level significance is 0,05% it implies that the teacher of English should use make a match assisted by flashcard in teaching reading comprehension so that the teaching and learning process can be successful.
A High-Efficiency FPGA-Based ORB Feature Matching System

Feature extraction and matching are the basic procedures of the so-called Visual Odometer (VO), Simultaneous Localization and Mapping (SLAM) and many other image processing algorithms. Oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (ORB) algorithm are widely used since they are computationally faster. In this paper, we proposed a method to generate a value for a feature, the value is called signature. In the matching step, we only compute Hamming distances of descriptors with the same signatures. Hence, the matching time is shortened. Compared with the original ORB algorithm, features to be matched dropped 69.63% on TUM datasets and 85.7% on VGG datasets by adopting our strategy. In addition, the precision is above 85% on both VGG and TUM datasets. We design a customized hardware architecture for ORB feature extraction and matching based on the proposed method. The hardware structure is implemented on Xilinx ZCU102 evaluation board. The clock frequency is set to 150[Formula: see text]MHz. Our Field Programmable Gate Arrays (FPGA) system achieves 193[Formula: see text]fps on [Formula: see text] images with 1984 features on average and 314[Formula: see text]fps on [Formula: see text] images with 700 features on average, which is more efficient compared to the state-of-the-art works.
RANCANG BANGUN SISTEM INFORMASI PENERIMAAN DAN PENGELUARAN DANA KAS PADA PERUSAHAAN DENGAN SAK (STANDAR AKUNTANSI KEUANGAN) YANG MENGGUNAKAN MODEL WATERFALL

Management of transactions such as cash receipts and disbursements at the company which are recorded in transaction notes which are then copied to Ms. Excel. To produce financial reports takes a long time, because the finance department must recapitulate one by one receipt and expenditure transactions to be used as cash receipts reports, cash disbursements reports, income statements, reports of changes in capital and balance sheets. Delays in the presentation of this report affect the decision-making process, especially those related to financial matters. In addition, there are often discrepancies or errors in the allocation of transactions by the finance department. The purpose of this research is to assist the company in managing data related to receipts and expenditures transactions to be faster and to produce financial reports quickly, on target and in accordance with Financial Accounting Standards (SAK). The model used in software development is the Waterfall model. The result of this research is to produce an accounting information system of cash receipts and disbursements providing processing of cash receipts and disbursements which are equipped with reports.
Application of Case Base Reasoning Algorithm in Detecting Disease in Pineapple Fruit

Pineapple fruit is a type of horticultural plant that has the potential to be developed. In the process of pineapple cultivation, it is very susceptible to pests and diseases. Diseases that often attack pineapples such as; wilt disease, stem base rot, fusariosis, bacterial rot, and urethral disease. The process of identifying pineapple diseases is often done manually so it takes a long time. In addition, in the process of controlling pests and diseases, farmers only spray pesticides or other handling techniques that are not suitable for the pests and diseases that attack them. Thus, the treatment is not optimal and has an impact on the emergence of new pests and diseases in pineapple. Currently, computer technology can be used in various branches of science, one of which is artificial intelligence. The expert system is a scientific branch of artificial intelligence that can solve problems. The purpose of this research is to assist farmers in identifying pests and diseases in pineapples so that the control process can be carried out optimally, quickly, and on target. The implementation of an expert system uses the Case Base Reasoning (CBR) method which will produce a diagnostic similarity value and provide recommendations for diseases that attack. This research processes data in the form of symptoms seen in pineapple plants. The test results obtained a percentage of 100%. Thus, the application of the CBR method is very relevant in identifying pests and diseases of pineapple plants.
Light-SAE: A Lightweight Authentication Protocol for Large-Scale IoT Environments Made With Constrained Devices

Due to the increasing demand for Internet of Things (IoT) applications with sensitive data, the use of encryption on constrained devices is crucial for protecting and ensuring privacy and security.The devices used in such applications have limited computational power, memory, and energy resources, making them vulnerable to attacks that exploit these limitations.Lightweight cryptography has been growing fast in recent years aiming to circumvent the lack of security in these types of devices.We propose a modular solution that capitalizes on the strengths of lightweight cryptography to offer an approach that can be easily adapted to meet the needs of private wireless sensor networks.The solution is designed to work in multihop communication networks, where Nodes out of range of the Gateway can be part of the network, offering the same security level that a Node in the communication range of the Gateway has.To further enhance the security and lifetime of the network, our solution incorporates a key renewal mechanism and supports the use of signature schemes for integrity.Additionally, it is designed to be distributed to achieve high levels of scalability.Experimental results show that using our solution on top of standard communication protocols does not introduce significant overhead in terms of performance.
Explosive Ideas for Pedagogy and Education, and the Processes of Spiritual Development

The article introduces the concept of “explosive ideas”, along with an emphasis on the diverse function of three types of “explosions” in the mode of meeting texts in culture. The author illustrates his practice of reading, lecturing, and writing books on the basis of five forms of “explosive effect”, inscribed in: (a) new meanings of well-known terms, destroying banality; (b) new terms as “screens”, revealing otherwise invisible phenomena; (3) eye-opening questions that encourage a change in perspective; (4) individual theses postulating paradoxicality, complexity, and new normativity of concepts; (5) sets of theses in the hermeneutic circle, constituting a well-established space for another being in culture. The text illustrates the phenomenon of explosiveness with examples of development impulses inscribed in the history of ideas and achievements of Nobel laureates in literature, treating the humanities as any self-reflection of culture, constituting an invisible symbolic soil for the development of spirituality. The explosiveness of an idea has three functions: (a) destroying naivety and appearances, experiencing a shock; (b) paving the way for accessing new meanings, opening the eyes with the effect of awakening; (c) stimulating internal transformation, rooting in life-giving spiritual impulses, decentrating our roots in world culture. Finally, the text reveals the idea of a “pandemic of thoughtlessness”. The article proposes a strategy for the educational treatment of culture, in which the affirmation of the “explosive” fragment coincides with the concern for the integral effect of influencing the rooting in the space of meanings. The author illustrates the explosiveness by citing many examples from the humanities as a diverse field of cultural self-reflection, taking into account literature, including poetry, and pointing to attempts to build theories as hypothetical representations of conceptual invariants of experience. The adopted strategy of reading belles lettres, history of philosophy, social disciplines, and ideas in the humanities is subordinated to the concern for the spiritual development of subsequent generations, inscribed in the symbolic heritage, and at the same time translates into pedagogical postulates.
Application of Google Meet’s Synchronous Learning and Asychronous E-Learning on Learning Effectiveness

Application of Google Meet's Synchronous Learning and Asynchronous E-Learning to Learning Effectiveness is a scientific article of literature study within the scope of Education Management science. The purpose of this article is to build a hypothesis of the influence between variables that will be used in further research. Research objects are sourced from online libraries, Google Scholar, Mendeley and other academic online media. Library research methods, from e-books and open access e-journals. The results of this study are: 1) Google Meet Synchronous Learning has an effect on Learning Effectiveness; 2) Asynchronous Learning E-Learning influences Learning Effectiveness; and 3) Google Meet Synchronous Learning affects E-Learning Asynchronous Learning.