Reviewerly/exHarmony huggingface dataset:
Analysis of Optimal Hyperparameters for EPI-based Structured Pruning

Network pruning is a widely used technique for reducing the memory and computational requirements of deep neural networks while minimizing performance degradation. Structured pruning, in particular, offers an effective approach without the need for additional hardware. However, many structured pruning methods require pre-trained large networks, increasing the overall training time. To address this challenge, this study proposes structured pruning using the early pruning indicator (EPI) to enable network compression from the initial training phase. Despite its effectiveness, this technique is sensitive to hyperparameter selection when applied to small datasets. This study analyzes hyperparameters for the application of EPI-based structured pruning to small datasets such as CIFAR10 and CIFAR100. By identifying optimal hyperparameters, the proposed method achieves performance scores of 92.19% and 69.27% on the CIFAR10 and CIFAR100 datasets, respectively, with a compression rate of 70% using the RestNet56 network.
3D Semantic Scene Completion With Multi-scale Feature Maps and Masked Autoencoder

Autonomous systems require a profound understanding of their surroundings, encompassing both semantic and 3D geometry. This study focuses on advancing 3D semantic scene completion approaches using a camera. Building upon the foundation laid by VoxFormer [1], which is recognized for its state-of-the-art performance in 3D semantic scene completion, our approach involves two distinct stages. In the initial stage, scene completion is done with depth images, while in the second stage, the final 3D scene completion is performed using masked autoencoder. To enhance the performance of VoxFormer, we introduced two key modifications. First, we modified the first stage using multi-scale feature maps. Second, we further modified the first stage using a masked autoencoder. Experimental results, based on the adapted VoxFormer model in both stages are presented. Our two proposed approaches exhibit notable improvements, particularly in the context of small objects. However, these enhancements warrant further investigation for optimization and refinement.
Improving Background Subtraction Algorithms with Shadow Detection

Motion detection of objects on video is a resource–intensive area of computer vision that requires several stages. The first stage of this task is the background subtraction process. The quality of further recognition fully depends on this stage. Natural or artificial illumination changes could provide a lot of shadows on the video. Such shadows could be falsely interpreted as parts of a moving object or even as the separate objects on the scene. This article proposes an improvement to the background subtraction algorithms by detecting and removing shadows from the images. The proposed approach relies onto spatial correlations between neighboring pixels in combination with the normal distribution of noise. It provides good background subtraction results along with low computational cost.
Scene Text Recognition With Dual Encoders

Despite significant advancements in scene text recognition, current models face substantial challenges, particularly when confronted with irregular text images featuring complex backgrounds, curved text, diverse fonts, and distortions. While convolutional neural network (CNN)-based text recognition networks have demonstrated commendable performance, they grapple with the aforementioned challenges. Recently, transformer-based feature extractors have exhibited advantages in global feature extraction from images, especially in the context of irregular text images. By employing self-attention, these transformers establish information connections between different parts of the image, thereby mitigating the impact of uneven character distribution. This study proposes multi-encoder scene text recognition (MESTR), a hybrid approach that combines a CNN-based and a transformer-based feature extractor. MESTR excels in simultaneously extracting local and global features from text images, ensuring the integration of both types of features to enhance performance. During training, we employed a guiding connectionist temporal classification (CTC) decoder [6] as a compensatory training strategy for the attentional decoder. Our experiments showed the efficacy of MESTR across seven benchmarks, demonstrating robust performance. In addition, ablation experiments are presented to validate the effectiveness of the proposed algorithm for scene text recognition.
High-speed physical layer secure communication scheme based on synchronous chaotic and electro-optic self-feedback dual-phase encryption

Abstract In this paper, a novel secure communication scheme based on temporal spreading and dual-phase perturbation is proposed to improve security at the physical layer. For one thing, the traditional electro-optical feedback phase encryption system is significantly sophisticated by the introduction of additional synchronized chaotic phase encryption. Besides, more keys in this system are available, with the attendance of dispersion and optical chaos, the key space is notably enhanced to ∼2 36 , resisting the eavesdropping attacks. After passing through a series of encryption devices, the information is ultimately displayed as a noise-like signal. A 32 Gb s −1 on-off keying signal is successfully transmitted over a 100 km standard single-mode fiber. Simulation results show that by selecting the appropriate system parameters, a bit error rate as low as 10 −10 can be obtained for legitimate terminals, and the effect of different hardware parameter mismatches on the decryption performance is also investigated which indicates a certain tolerance range for the mismatch of each parameter. This paper provides a new idea to combine an electro-optical feedback system with optical chaos to establish optical secrecy communication.
Identification of Vulnerable Kernel Code Using Kernel Tracing Mechanism

Vulnerable kernel code is a threat to an operating system kernel. An adversary's user process can forcefully invoke vulnerable kernel code to cause privilege escalation or denial of service (DoS). Although security engineers belong Computer Security Incident Response Team (CSIRT) and Product Security Incident Response Team (PSIRT) of service providers or security operators have to consider the effect of kernel vulnerability on their system environment when deciding whether or not to update a kernel, the list of vulnerable kernel code is not provided in the Common Vulnerabilities and Exposures (CVE) report In addition, it is difficult to identify the vulnerable kernel code from the exploitation results in the kernel which indicates the account or the kernel suspension information. To identify the details of kernel vulnerability, we propose a vulnerable kernel code tracer (vkTracer), which uses an alternative viewpoint using proof-of-concept (PoC) code to create a profile of kernel vulnerability. vkTracer traces the user process of the PoC code and the running kernel to hook the invocation of the vulnerable kernel code. Moreover, vkTracer extracts the whole kernel component's information using the running and static kernel image and debug section. This ensures that vkTracer identifies the virtual address range and function name of the invoked kernel code from the traced user process; a profile of kernel vulnerability is then created. The evaluation results indicate that vkTracer could trace PoC code execution (e.g., privilege escalation or DoS), identify vulnerable kernel code, and generate a kernel vulnerability profile. Additionally, vkTracer could trace the kernel code of system call invocation regarding CVE information. Furthermore, the implementation of vkTracer reveals that the identification overhead ranges from 5.2683 s to 5.2728 s on the PoC codes and the acceptable system call latency is 3.7197 µs. Moreover, vkTracer represents 0.37% and 0.56% of the dynamic kernel tracing overhead for the web client program access overhead of 100,000 Hypertext Transfer Protocol sessions.
E-LEARNING SAU REIFICAREA ÎNVĂȚĂRII

Starting from the social transformations and directions, we accept, volens nolens, that technology has long sat down at the teacher’s desk. We have also grown accustomed that its teaching strategy has nothing to do with the former classical teaching style anymore, no matter how modern it has become with its new teaching methods. So, regardless of its practical form (TV lesson, distance learning through different platforms), learning takes more and more practical and pragmatic shapes. It tends not to represent a complex formation which generates intellectual, cultural, moral abilities anymore, but a kit of competences, seemingly independent from the fundamental human values. Traditionally, learning takes place thanks to human interaction. Presently it seems to lose its constitutive elements and to proclaim bombastically the supremacy of the digital world that asks to be reified, trusted by heart; a surrogate with a trademark status. Will learning represent an intellectual effort, an effort of originality, competence and consequently of value, or will it sit comfortably in an „effort” of searching, juxtaposition and conformation to the immediate imperatives? In other words: brainly or library, seminar/debate/maieutics or chat GBT?
Inverse Quasi Cyclic Code and Application in Chain Ring

The existence of inverse quasi-cyclic codes in the 2 2 F F + uring is presented. Here, Gray transform and inverse quasi cyclic code definitions are given first. Afterwards, its relationship with automorphism and cyclic codes is mentioned. The proposition including Nechaev permutation, quasi cyclic codes and Gray transformation is explained. From this it is concluded that inverse quasi cyclic codes correspond to Gray images of the cyclic code. Finally, a 4-length repeat code example is included regarding this topic.
A Survey of The State-Of-The-Art AutoML Tools and Their Comparisons

Machine learning is used effectively in many areas today and its usage area is increasing day by day. In addition, processes based on machine learning are also developing in a technology-oriented manner, and users are gaining new perspectives on solving current problems. While machine learning makes predictions about stocks in the financial sector, it also plays an active role in early diagnosis of diseases in the healthcare sector. It is actively used in route calculation and defective product detection in the field of production and logistics, and in situations such as analysis of customer behavior and product recommendations in the shopping sector. AutoML can be defined as a process that aims to automate the machine learning process end-to-end. It enables the machine learning process to be accelerated by automating especially time-consuming tasks that work with the logic of repetition, and it allows people who work in this field to create more efficient and productive models. In addition, AutoML helps users who are not experts in this field in the stages of machine learning model development, data management, analysis and evaluation of their own data, by providing various conveniences to users in model training and subsequent stages. In this article, after discussing what AutoML is, AutoML processes and areas of use, information about various AutoML platforms have been given, the differences between widely used AutoML platforms will be evaluated, and their advantages and disadvantages compared to each other will be included.
Assessing and mitigating privacy risks of sparse, noisy genotypes by local alignment to haplotype databases

Single nucleotide polymorphisms (SNPs) from omics data create a reidentification risk for individuals and their relatives. Although the ability of thousands of SNPs (especially rare ones) to identify individuals has been repeatedly shown, the availability of small sets of noisy genotypes, from environmental DNA samples or functional genomics data, motivated us to quantify their informativeness. We present a computational tool suite, termed Privacy Leakage by Inference across Genotypic HMM Trajectories (PLIGHT), using population-genetics-based hidden Markov models (HMMs) of recombination and mutation to find piecewise alignment of small, noisy SNP sets to reference haplotype databases. We explore cases in which query individuals are either known to be in the database, or not, and consider several genotype queries, including those from environmental sample swabs from known individuals and from simulated “mosaics” (two-individual composites). Using PLIGHT on a database with ∼5000 haplotypes, we find for common, noise-free SNPs that only ten are sufficient to identify individuals, ∼20 can identify both components in two-individual mosaics, and 20–30 can identify first-order relatives. Using noisy environmental-sample-derived SNPs, PLIGHT identifies individuals in a database using ∼30 SNPs. Even when the individuals are not in the database, local genotype matches allow for some phenotypic information leakage based on coarse-grained SNP imputation. Finally, by quantifying privacy leakage from sparse SNP sets, PLIGHT helps determine the value of selectively sanitizing released SNPs without explicit assumptions about population membership or allele frequency. To make this practical, we provide a sanitization tool to remove the most identifying SNPs from genomic data.
Methodology to improve the predictability of solar energy generation: preliminary evidence from Great Britain

Using data from Great Britain, this paper begins by observing that the current solar energy forecasts are inaccurate even though the capacity-weighted metric of the forecast error suggests otherwise. This occurs because weighting the mean absolute error by capacity will yield a biased assessment of the forecast error when technologies with a low capacity factor, such as solar energy generation, are evaluated. Consistent with the view that the capacity-weighted metric of the forecast error can lead to a misleading assessment of forecast accuracy, the paper&apos;s analysis indicates that the forecasts do not fully reflect expected weather information. It is also observed that solar energy generation is highly autoregressive, which can be modeled to improve predictability. An Autoregressive Moving Average with Exogenous Inputs (ARMAX) model is formulated based on those properties. The model was estimated using 30-minute data from Dec 7, 2018, through Dec 31, 2021. The model is evaluated using out-of-sample data from Jan 1, 2022, to Jun 30, 2022. The period-ahead out-of-sample predictions have a weighted-mean-absolute-percentage-error (WMAPE) of about 3.3 %, substantially less than the approximately 14.7% WMAPE associated with the solar energy forecasts used by the system operator over the same period.
Application and Research on a Large Model Training Method Based on Instruction Fine-Tuning in Domain-Specific Tasks

The potential of large-scale models to enhance industrial productivity and catalyze societal progress is undeniable. However, inherent challenges-such as lengthy training cycles and the demand for advanced computational resources-remain daunting. Given recent advancements in computational adaptability, this paper introduces a systematic approach to effectively fine-tune these models for domain-specific tasks. Our method encompasses three key phases: (1) a thorough analysis of domain-specific business needs and data acquisition; (2) precise task segmentation, designing standardized instruction formats to construct a fine-tuning dataset, and subsequently fine-tuning the large-scale models; (3) rigorous model validation using a test dataset. Through these steps, we effectively fine-tuned our training using 5,000 data instances and validated our results with an additional 1,000 test instances. To complement our study, we provide a comparative analysis of different training techniques and assess the fine-tuning results on four prominent open-source models. The conclusions drawn offer valuable insights for the future application of large-scale models in specialized domains and pave the way for further research and applications.
TolerantGAN: Text-guided Image Manipulation Tolerant to Real-world Image

Although text-guided image manipulation approaches have demonstrated highly accurate performance for editing the appearance of images in a virtual or simple scenario, their real-world applications face significant challenges. The primary cause of these challenges is the misalignment in the distribution of training and real-world data, which leads to unstable text-guided image manipulation. In this work, we propose a novel framework called TolerantGAN and tackle the new task of real-world text-guided image manipulation independent of the training data. To achieve this, we introduce two key concepts of a border smoothly connection module (BSCM) and a manipulation direction-based attention module (MDAM). BSCM smoothens the misalignment in the distribution of training and real-world data. MDAM extracts only regions highly relevant for image manipulation and assists in reconstructing unobserved objects in the training data. For in-the-wild input images of various classes, TolerantGAN robustly outperforms the state-of-the-art methods.
Research on Sports and Psychological Health Evaluation of College Sudents Based on Fuzzy Clustering Algorithm

Clustering analysis is one of the main tasks of data mining, which can be used as an independent tool to obtain the distribution of data and classify the objects according to their characteristics. Fuzzy clustering analysis is based on the data characteristics of the object itself. According to the characteristics, closeness and similarity of the objective things, it can cluster the objective things by establishing a fuzzy similarity relationship to compare the similarity of different students' sports and psychological health. Based on the principle and method of fuzzy clustering analysis, the mathematical model is constructed, the fuzzy clustering process is designed, the evaluation index system is constructed, and the data preprocessing method is pointed out. The research results provide a complete solution for college students' sports and psychological health evaluation, and provide a reference for other fuzzy cluster evaluation.
Development of a Procedure for Improving the Effectiveness of Personal Identification Systems Based on the Analysis of Facial Images

This research aims to develop a new procedure to improve the performance of the personal identification system. This process will be based on the analysis of digital facial images using special artificial intelligence technologies. Additionally, it is emphasized that the successful development of a completely new neural network algorithm for personality identification and its special robust modification are required to achieve this goal. Furthermore, it is essential that this algorithm demonstrates increased effectiveness in identifying faces. As part of this algorithm, two key components are distinguished, namely, an algorithm aimed at determining the area where a face is located in an image, which involves the use of a standard version of a convolutional neural network. And the presented neural network algorithm is the second component, which is designed for personal identification with a specific robust modification. The studies presented have shown that an algorithm with a proposed architecture, will have around 25 times less weight parameters than the algorithms utilized at present. Also, unlike other algorithms, it was possible to improve the level of accuracy.
Evaluation of Convolutional Neural Network Model Architecture Performance

The identification of image types can be facilitated through the use of algorithms and automation in their detection. In this research, detection is performed on fashion images, which encompass various types and categorizations. The process of identification and detection can utilize the Neural Network algorithm known as Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) algorithm consists of multiple layers, and its application involves setting values for pixels as well as arranging the sequence of layers within it. In this study, two Convolutional Neural Network (CNN) algorithm models are employed for image classification in the fashion dataset. These two models differ in the order of layers within the Convolutional Neural Network (CNN) and in the values of strides applied for modification. Model 1 employs ZeroPadding2D in the first layer with a smaller stride value of 1 compared to Model 2, which places the Conv2D layer in the first layer and applies strides with 1, 2, and 3 values. This research indicates that Model 1 outperforms Model 2, achieving the highest accuracy in epoch 20 with an accuracy value of 0.9711. This is attributed to the fact that the strides value in the Conv2D layer of Model 1 is set to a smaller value compared to Model 2.
Privacy-Preserving Location Sharing via LWE-based Private Information Retrieval

We proposes a privacy-preserving location sharing scheme based on Learning with Errors (LWE) encryption, emphasizing its ability to resist quantum computing attacks and provide a high level of security. In order for real-world usage, we focus on its efficiency, flexibility, and adaptability for streaming. Location privacy has has emerged as a critical concern in today's data-driven society, due to the widespread usage of location-based services and the growing demand for personalized information. We leverages for fast and accurate information retrieval while maintaining robust privacy guarantees, achieving peer-to-peer transmission, 128-bit security or higher and an unparalleled throughput of 10 GB/s/core Through extensive experimental evaluations conducted on real-world deployments, we demonstrate the remarkable performance of our scheme in terms of preserving privacy and retrieval speed, particularly for streaming location data.
NURBS Curve-Based Trajectory Planning for Craniotomy Robot

Since the surface of human skull is irregularly curved, the surgical path is a three-dimensional curve with complex curvature. In order to fit the motion trajectory to the surgical path during the craniotomy performed by the robot-assisted surgeon, this paper realizes the trajectory planning of the surgical robot in Cartesian space by establishing a cubic non-uniform rational basis spline (NURBS) curve model based on the isosceles difference linear approximation method. Firstly, the key points of the surgical path are extracted as type value points. Then the control points are back-calculated. Finally, the cubic NURBS curve model for fitting the surgical path is established. On this basis, the cubic NURBS curve is interpolated using the isosceles difference linear approximation method to provide reasonable cutting steps for the physician's hand-held robot milling. Finally, the experiments show that the cubic NURBS curve model can accurately fit a smooth surgical path after the defined type-valued point coordinates. The motion trajectory after interpolation with the isosine difference linear approximation method can ensure robot motion smoothly.