Reviewerly/exHarmony huggingface dataset:
tsrCert: Traceable Self-Randomization Certificate and Its Application to Blockchain Supervision

Traditional public key infrastructure (PKI) only provides authentication for network communication, and the standard X.509 certificate used in this architecture reveals the user's identity. This lack of privacy protection no longer satisfies the increasing demands for personal privacy. Though an optimized anonymous PKI certificate realizes anonymity, it has the potential to be abused due to the lack of identity tracking. Therefore, maintaining a balance between user anonymity and traceability has become an increasing requirement for current PKI. This paper introduces a novel traceable self-randomization certificate authentication scheme based on PKI architecture that achieves both anonymity and traceability. We propose a traceable self-randomization certificate authentication scheme based on the short randomizable signature. Specifically, certificate users can randomize the initial certificate and public key into multiple anonymous certificates and public keys by themselves under the premise of traceability, which possesses lower computational complexity and fewer interactive operations. Users can exhibit different attributes of themselves in different scenarios, randomizing the attributes that do not necessarily need to be displayed. Through security and performance analysis, we demonstrate the suitability of the improved PKI architecture for practical applications. Additionally, we provide an application of the proposed scheme to the permissioned blockchain for supervision.
Neural Network Based Traffic Monitoring using UAVs

In large and growing metropolitan areas, the rise in traffic congestion is becoming an inescapable problem. It is estimated that the traffic congestion in metro cities costs the nation approximately 1.5 lakh crore rupees every year. With the increase in congestion, accident rate increases proportionally. The reckless driving and increased speed are the root cause of road accidents. We propose a speed detection algorithm to detect and monitor the speed of vehicles crossing a certain threshold speed limit. On national highways, the long queues at toll booths lead to loss of time and money. We propose image processing and convolutional neural network based algorithm to address the problem of traffic congestion, ease the flow of traffic, anomalies detection and ultimately reduce pollution and fuel consumption.
Improved DetNet Algorithm Based on GRU for Massive MIMO Systems

Massive Multiple-Input Multiple-Output (MIMO) technology is widely used to achieve high system capacity and spectral efficiency in wireless communication systems. The application of deep learning algorithms in massive MIMO signal detection has attracted great attention with the development of artificial intelligence in recent years. DetNet is a deep detection network for massive MIMO systems. By introducing the reset gate and update gate mechanism of the gate recurrent unit, this paper proposes an improved massive MIMO detection algorithm GRU-DetNet. Then, a hybrid neural network (Hybrid-DetNet) model with a parallel structure is further proposed. Simulation results show that the proposed GRU-DetNet and the Hybrid-DetNet achieve about 0.5 dB and 1 dB performance gain respectively over the existing DetNet scheme. The running time is reduced by about 50% under the same conditions. In addition, the proposed method has good universality since it can handle various modulation modes by training only a single network.
Sparrow Search Algorithm Based on Cubic Mapping and Its Application

Sparrow search algorithm (SSA) easily appears some problems such as falling into local optima and lacking search precision. To address these issues, a sparrow search algorithm based on cubic mapping (CSSA) improvement is proposed. Wanting a better population, a Cubic chaotic initialization method is used to improve population quality. During the producer position update process, levy fight is added to widen the search area of the algorithm. Then, an inertia weight is introduced during the scrounger phase. The algorithm is tested using CEC2022 test functions and compared with multiple algorithms. The data show that CSSA can overcome the problem that SSA easily getting stuck in local optimum to some extent, and improve convergence accuracy and stability. At last, CSSA is applied to an engineering problem of three-bar truss, and compared with other algorithms in a comparative experiment.
DCNet: Glass-Like Object Detection via Detail-Guided and Cross-Level Fusion

Glass-like object detection aims to detect and segment whole glass objects from complex backgrounds. Due to the transparency of glass, existing detection methods often suffer from blurred object boundaries. Recently, several methods introduce edge information to boost performance. However, glass boundary pixels are extremely sparser than others. Using only edge pixels may negatively affect the glass detection performance due to the unbalanced distribution of edge and non-edge pixels. In this study, we propose a new detail-guided and cross-level fusion network (which we call DCNet) to tackle the issues of glass-like object detection. Firstly, we exploit label decoupling to get detail labels and propose a multi-scale detail interaction module (MDIM) to explore finer detail cues. Secondly, we design a body-induced cross-level fusion module (BCFM), which effectively guides the integration of features at different levels and leverages discontinuities and correlations to refine the glass boundary. Finally, we design an attention-induced aggregation module (AGM) that can effectively mine local pixel and global semantic cues from glass-like object regions, fusing features from all steps. Extensive experiments on the benchmark dataset illustrate the effectiveness of our framework.
Nonlinear Inertia Weight Whale Optimization Algorithm with Multi-strategy and Its Application

Whale optimization algorithm (WOA) suffers from slow convergence speed, low convergence accuracy, also difficulty in escaping local optima. To address these issues, we propose an improved whale algorithm that incorporates Latin hypercube sampling for population initialization. This ensures a more uniform distribution of the population in the initial stage compared to the random initialization. And then, introducing Cauchy Distribution into WOA’s searching prey stage. This prevents premature convergence to local optima and avoids affecting the later convergence. Furthermore, applying a nonlinear inertia weight to make an improvement on the convergence speed and accuracy of the algorithm. And compared the improved whale algorithm with other algorithms using 18 benchmark functions, and all results given indicated that the proposed algorithm better than original WOA and other algorithms. Finally, applying the improved WOA, original WOA and a mutate WOA to optimize the design of a pressure vessel, and the optimization results demonstrated that the effectiveness of the proposed method.
Improved Database Security Using Cryptography with Genetic Operators

Database security is of utmost importance in today’s digital age where sensitive data is vulnerable to unauthorized access and cyber-attacks. Various security models have been developed for databases, each concentrating on a different part of the database security challenge. This paper presents a novel approach to improve database security by leveraging the combination of cryptography and genetic operators. The proposed technique involves encrypting the data using cryptographic algorithms, ensuring its confidentiality even in the event of a compromised database login. Additionally, genetic operators are employed to enhance the encryption process, making it more robust against potential vulnerabilities. Through the encryption and comparison mechanisms, only authorized users with the correct credentials can access and decipher the data, providing an additional layer of protection. The effectiveness of this approach is demonstrated through its ability to mitigate common causes of database vulnerabilities and safeguard valuable information. Furthermore, future research directions are suggested further to enhance the efficiency and effectiveness of the encryption algorithms, ultimately strengthening the overall security of databases.
Feature point detection in HDR images based on coefficient of variation

Feature point (FP) detection is a fundamental step of many computer vision tasks. However, FP detectors are usually designed for low dynamic range (LDR) images. In scenes with extreme light conditions, LDR images present saturated pixels, which degrade FP detection. On the other hand, high dynamic range (HDR) images usually present no saturated pixels but FP detection algorithms do not take advantage of all the information present in such images. FP detection frequently relies on differential methods, which work well in LDR images. However, in HDR images, the differential operation response in bright areas overshadows the response in dark areas. As an alternative to standard FP detection methods, this study proposes an FP detector based on a coefficient of variation (CV) designed for HDR images. The CV operation adapts its response based on the standard deviation of pixels inside a window, working well in both dark and bright areas of HDR images. The proposed and standard detectors are evaluated by measuring their repeatability rate (RR) and uniformity. Our proposed detector shows better performance when compared to other standard state-of-the-art detectors. In uniformity metric, our proposed detector surpasses all the other algorithms. In other hand, when using the repeatability rate metric, the proposed detector is worse than Harris for HDR and SURF detectors.
Cross-media Retrieval via Fusing Multi-modality and Multi-grained Data

Traditional cross-media retrieval methods mainly focus on coarse-grained data that reflect global characteristics, while ignoring the fine-grained descriptions of local details. Meanwhile, traditional methods cannot accurately describe the correlations between the anchor and the irrelevant data. To solve the problems mentioned above, this paper proposes to fuse coarse-grained and fine-grained features and a multi-margin triplet loss on the basis of a dual-framework. 1) Framework I: a multi grained data fusion framework based on Deep Belief Network, and 2) Framework II: a multi-modality data fusion framework based on the multi-margin triplet loss function. In Framework I, the coarse grained and fine-grained features fused by the joint Restricted Boltzmann Machine are input into Framework II. In Framework II, we innovatively propose the multi-margin triplet loss. The data, which belong to different modalities and semantic categories, are stepped away from the anchor in a multi-margin way. Experimental results show that the proposed method achieves better cross-media retrieval performance than other methods with different datasets. Furthermore, the ablation experiments verify that our proposed multi-grained fusion strategy and the multi-margin triplet loss function are effective.
Analysis of Student Management in Vocational Colleges from the Perspective of Whole-life Management

In the new era, student management work in vocational colleges should truly adhere to the "student-centered" educational management philosophy.It emphasizes and recognizes the students' central role in educational management, taking the principle of promoting students' development as the core value of educational management work.By doing so, the effectiveness of student management in vocational colleges can
Hyperparameter Tuning for an Enhanced Self-Attention-Based Actor-Critical DDPG Framework

In a research environment with growing demand for autonomous agents to perform human-level tasks, reinforcement learning (RL) is central. Through our research experiments, we tend to analyze the effect hyperparameters, such as learning rate and their tuning in various configurations of optimizers and regularization techniques, can have on the performance of policy learning for RL agents. With various modern-day applications such as self-driving cars gaining momentum, we chose a simple but relatable environment of Pendulum. In simple words, we want the pendulum to defy gravity and learn balance itself at the highest position. The experiments used two actor-critic networks to add stability to policy learning. We also used a buffer for experience replay those aids in faster convergence of the networks. We concluded the various hypotheses stated above to be accurate and present some trends observed during the experiments. We tested a self-attention-based architecture for both the actor and the critic networks along with a self-attention architecture for the target actor and critic networks. We tried the various combinations such as having self-attention-based architecture for target and not for the policy network, vice versa, and having fully connected network components in both networks. The results suggest that having self-attention in both pair of networks works better than the rest, and this can be attributed to the advantages of the having a self-attention-based architecture. The way the weights of the policy network are updated based on the updates of target network in the previous literature does not take into account the reward; here in this study, based on the value of loss, we change the tau value—higher tau if the loss is higher and lower tau if the loss is lower. The Deep Deterministic Policy Gradient (DDPG) model trained on proposed work reaches −600 rolling average of reward in almost half of the time (80 episodes) when compared to the model trained normally (150 episodes).
Feature fusion-based text information mining method for natural scenes

Abstract As a crucial medium of information dissemination, text holds a pivotal role in a multitude of applications. However, text detection in complex and unstructured environments presents significant challenges, such as the presence of cluttered backgrounds, variations in appearance, and uneven lighting conditions. To address this issue, this study proposes a text detection framework that leverages multistage edge detection and contextual information. This framework deviates from traditional approaches by incorporating four primary processing steps, including text visual saliency region detection to accentuate the text regions and diminish background interference, multistage edge detection to enhance the conventional stroke width transform results, a texture-based and connected components-based integration to accurately distinguish text from the background, and a context fusion step to recover missing text regions and improve the recall of text detection. The proposed method was evaluated on two widely used benchmark datasets, i.e., the international conference on document analysis and recognition (ICDAR) 2005 dataset and the ICDAR 2011 dataset, and the results indicate the advancedness of the method.
The Application of Smart Classroom Management System to Cultivate College Students’ Independent Learning Ability in Higher Education Institutions

The deep integration of network technology and education has resulted in a variety of developments in the field of education, and the digital education model has steadily evolved towards a higher educational growth path such as "intelligent education" or "future education".As the primary means of teaching and educating people in the age of intelligent education, the smart classroom is critical to realizing the change of education from knowledge talents to intelligent talents.This research discovers that wisdom classroom practices the concept of "student-centered" and is changing to the construction concept of "enabling students' intelligent development" from the standpoint of nurturing college students' autonomous learning ability.At the same time, this paper discusses the importance and value of smart classrooms through a systematic analysis of higher vocational teaching activities in a smart classroom environment, and proposes how to establish a smart classroom management system to cultivate the independent learning ability of college students in higher vocational colleges, in order to enrich and improve the smart classroom education system and teaching mechanism in higher vocational colleges.
Privacy-Preserving Machine Learning for Healthcare: Open Challenges and Future Perspectives

Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this paper, we conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service, and perform a comprehensive review of existing trends, identify challenges, and discuss opportunities for future research directions. The aim of this review is to guide the development of private and efficient ML models in healthcare, with the prospects of translating research efforts into real-world settings.
Using Visual Cryptography and hash function for Fragile Watermarking to Detect Electronic Document Forgery

Recent developments in technology and the area of digital communications have rendered digital images increasingly susceptible to tampering and alteration by persons who are not authorized to do so. This may appear to be acceptable, especially if an image editing process is necessary to delete or add a particular scene that improves the quality the image. But what about images used in authorized governmental transactions? The consequences can be severe; any altered document is considered forged under the law and may cause confusion. Also, any document that cannot be verified as being authentic is regarded as a fake and cannot be used, inflicting harm on people. The suggested work intends to reduce fraud in electronic documents using a novel generator. It employs modern encryption methods to conceal the data represented by the watermark and hide it secretly inside the document so that it is easy to check for signs of counterfeiting; thus, allowing for the discovery of forged papers
Media Literacy Based Android Application to Introduce Sumbawa Culture for Visual Disabilities

This research aims to introduce Sumbawa culture to blind disability based android smartphone applications that can utilize feature reader screens and barcodes on objects historical. Output from application This is information in the form of audio details of cultural sites in the Inner Palace Loka, Bala Kuning Palace, Bala Puti Palace, Bala Datu Ranga, and the Sumbawa Museum. This research is quantitative method with build uses waterfall method and design with UML models with test six aspect appropriateness to application. The resulting average value for the attractiveness p ax is 1.90 and the average score for as a perspicuity peak of 1.77. Third aspect from UEQ, efficiency value with an average of 1.61 and dependability aspects with value 1.55. Whereas the stimulation aspect on the UEQ model is 1.49 and the novelty aspect with the average value is 1.39. From testing aspect perspicuity, efficiency, dependability, stimulation and novelty show quality application are excellent. Whereas the attractiveness aspect generates very good value. Application literacy culture for blind disability is something effort in preserving culture and can be accessible on google play store.
TDRConv: Exploring the Trade-off Between Feature Diversity and Redundancy for a Compact CNN Module

Rich or even redundant features without losing diversity of feature maps can undoubtedly help to improve network performance. In this work, we propose a compact CNN module by exploring the trade-off between feature diversity and redundancy, namely TDRConv, to retain and generate features with moderate redundancy and rich diversity but require less computation. Specifically, the input features are split into the main part and the expansion part by certain proportion, where the main part can extract intrinsic and diverse features in different ways, while the expansion part can enhance the extraction ability of diverse information. Finally, a series of experiments are conducted to verify the effectiveness of the proposed TDRConv on CIFAR10 and ImageNet. The results show the network models equipped with TDRConv can all outperform the state-of-the-art methods in terms of accuracy, but with significantly lower FLOPs and parameters. More importantly, the proposed TDRConv can readily replace the existing convolution modules as a plug-and-play component, and it is promising to further extend CNNs to wider scenarios.
Time-Resolved Determination Of Velocity Fields On The Example Of A Simple Water Channel Flow, Using A Real-Time Capable FPGA-Based Camera Platform

Field Programmable Gate Arrays (FPGAs) have become a widely available computational resource for real-time image processing and can be used in time-critical optical process measurements. This study investigates the applicability of the spatial filtering technique for time-resolved velocity field determination, on the example of a simple water channel flow. Therefore, the spatial filtering technique was implemented for a custom FPGA platform and analyzed regarding the effect of spatial and temporal resolution on the accuracy of the results. It was possible to prove the algorithm as suitable for velocity field estimation and identify algorithm parameters, which allow increasing the accuracy of the results.
Analisis Kemampuan Menulis Karya Tulis Ilmiah Mahasiswa Program Studi Pendidikan Teknologi Informasi STKIP Taman Siswa Bima

This qualitative field research aims to analyze students' writing skills in scientific writing. The data source comprises students from the Information Technology Education Study Program at STKIP Taman Siswa Bima, specifically those enrolled in Indonesian language courses during the odd semester of 2022/2023. The data collection method utilized tests, while data analysis followed the Miles and Huberman models. Findings indicate that students' writing skills scored an average of 72.3% across various aspects, including content of ideas (22.2%), organizational content (14.15%), grammar (14.2%), vocabulary (17.65%), and spelling/writing (4.1%). Overall, the study concludes that students' ability to write scientific papers in the Information Technology Education Study Program at STKIP Taman Siswa Bima is at a good level.
User’s Satisfaction Analysis of the Academic Information Systems Quality using the Modified Webqual 4.0 Method and Importance-Performance Analysis

Currently, the academic information system (AIS) at universities processes academic data to facilitate student’s activities. AIS was developed to provide maximum service to students. To optimize the use of information technology and to ensure the appropriateness of the provided AIS services, it is necessary to examine the level of service provided to improve quality. This study aimed to analyze the level of AIS service quality based on user perceptions and expectations. Dissemination of online questionnaires using Google Forms with a total of 100 students as respondents. This study used the modified Webqual 4.0 method as an indicator in the preparation of the questionnaire and the importance-performance analysis (IPA) method as an analysis method. The results of data were classified based on the percentage of user’s satisfaction with AIS services with three classifications, namely good, moderate, and poor. The results of the IPA analysis showed that the AIS had good quality. The results obtained from the analysis of the quality of the AIS system had a conformity level of 90.90%, where respondents perceived close to satisfaction with AIS services. The gap level was -0.3281 which was the result of the perception/performance of the AIS that was not in line with the expectations of the user. The results of this study contribute to Universitas Muhammadiyah Gombong as reference material and evaluation of AIS system services in the future.