Reviewerly/exHarmony huggingface dataset:
Precise GANs Classification Based on Multi-Scale Comprehensive Performance Analysis

GAN (Generative adversarial network) is a type of deep learning model that can generate fake data that looks real.GAN consists of two rival neural networks generator and discriminator.There are several types of GAN can be classified based on different criteria such as learning method, network architecture, application, and improvement of training.under the umbrella of learning methods, GAN can be categorized into three types, including unsupervised, semi-supervised, and supervised learning methods.Fully Connected GANs, Convolutional GANs, LAPGANs, AAEs, and Vari GANs are all subtypes of GANs that are distinguished by their respective network architectures, the quantity and composition of layers used in each style distinguishes it from the others.Application areas for GAN include computer vision and image processing, medical imaging, natural language processing, cyber security, and fault detection and tracking.In conclusion, GAN can be categorized in accordance with how training has progressed such as feature matching, probability percentages, regularization, choosing proper optimization, adding noise to discriminator, hyperparameters tuning, normalization, and weight normalization.
Defending AI-Based Automatic Modulation Recognition Models Against Adversarial Attacks

Automatic Modulation Recognition (AMR) is one of the critical steps in the signal processing chain of wireless networks, which can significantly improve communication performance.AMR detects the modulation scheme of the received signal without any prior information.Recently, many Artificial Intelligence (AI) based AMR methods have been proposed, inspired by the considerable progress of AI methods in various fields.On the one hand, AI-based AMR methods can outperform traditional methods in terms of accuracy and efficiency.On the other hand, they are susceptible to new types of cyberattacks, such as model poisoning or adversarial attacks.This paper explores the vulnerabilities of an AI-based AMR model to adversarial attacks in both single-input-single-output and multiple-input-multiple-output scenarios.We show that these attacks can significantly reduce the classification performance of the AI-based AMR model, which highlights the security and robustness concerns.Therefore, we propose a widely used mitigation method (i.e., defensive distillation) to reduce the vulnerabilities of the model against adversarial attacks.The simulation results indicate that AI-based AMR model can be highly vulnerable to adversarial attacks, but their vulnerabilities can be significantly reduced by using mitigation methods.
Trends of Optimization Algorithms from Supervised Learning Perspective

Machine learning (ML) is rapidly evolving, leading to numerous theoretical advancements and widespread applications across multiple fields. The goal of ML is to enable machines to carry out cognitive tasks by acquiring knowledge from past encounters and resolving intricate issues despite varying circumstances that deviate from previous instances. Supervised Learning (SL) being one of the most popular type of ML has become an area of significant strategic importance due to its practical applications, data collection, and computing power's exponential growth. On the other hand, optimization is a crucial component of ML that has garnered significant attention from researchers. Numerous proposals have been made one after another for solving optimization problems or enhancing optimization techniques in the field of ML. A comprehensive review and application of optimization methods from the perspective of ML is crucial to guide the development of both optimization and ML research. This article presents information specifically on the area of SL and a wide range of optimization methods, applied in conjunction to address various scientific issues. Additionally, this article explores some of the challenges and open problems in optimizing SL models.
3D Trajectory Planning of Agricultural Unmanned Aerial Vehicles Based on GA–SA Algorithm

The trajectory optimization problem in agricultural unmanned aerial vehicles (UAVs) inspection of agricultural facilities and equipment shares similarities with the traveling salesman problem.Traditional heuristic algorithms can encounter challenges such as overlapping flight paths and local optima due to complex terrain, numerous obstacles, and external environmental influences.To optimize the agricultural UAV detection process, a Genetic Algorithm and Simulated Annealing (GA-SA) algorithm based on GA was proposed to calculate 3D flight paths for agricultural UAVs.By establishing a mathematical model that combines the UAV threat environment with physical constraints, the cost function of the traditional SA algorithm was optimized to improve search efficiency.Additionally, trajectory smoothing techniques were applied to ensure smooth transitions between trajectory points.Experimental results demonstrate that the GA-SA algorithm overcomes the limitations of the traditional SA algorithm in node search efficiency and trajectory smoothing, enabling the planning of realistic and optimal flight paths in 3D environments.The GA-SA algorithm exhibits an 18.82% improvement in convergence time GA-SA compared with the traditional GA algorithm and a 13.20% improvement compared with the SA algorithm.Moreover, it shortens the optimal track distance by 2.244 km and 1.793 km, respectively, validating the effectiveness of the proposed method.
Media Pembelajaran Berbasis Android pada Mata Pelajaran Simulasi dan Komunikasi Digital Kelas X di SMK Gema Nusantara Bukittinggi

Penelitian ini dilatarbelakangi pada pembelajaran Simulasi dan Komunikasi Digital bahwa sistem pembelajaran masih menggunakan metode ceramah. Pada masa Covid-19 ini mengakibatkan pengurangan jam pembelajaran dari 45 menit menjadi 30 menit, pembelajaran yang berlangsung secara daring memberikan pengaruh terhadap guru dalam menyampaian materi pembelajaran yang terbatas dan kemunduran dari segi pemahaman siswa pada kelas X di SMK Gema Nusantara Bukittinggi. Penelitian ini menggunakan metode pengembangan atau Research and Development (R&amp;D) dengan model pengembangan Multimedia Development Life Cycle (MLDC) oleh Luther-Sutopo yang terdiri atas enam tahap, yaitu tahap Concept, Design, Material Collecting, Assembly, Testing, dan Distribution. Hasil dari prnelitian ini berupa media pembelajaran bebasis android yang dapat digunakan oleh guru maupun siswa dimanapun dan kapanpun berada. Hasil uji validitas dilakukan 3 orang ahli yaitu ahli komputer, bahasa indonesia dan materi TIK dengan rata-rata 0,8 kategori valid. Hasil praktikalitas oleh 3 orang penguji yaitu guru mata pelajaran Simulasi dan Komunikasi Digital di SMk Gema Nusantara Bukittinggi, dosen pendidikan IAIN Bukittinggi Dan dosen Komputer IAIN Bukittinggi dengan mendapatkan nilai rata-rata 0,91 katogeri praktis. Uji efektifitas oleh 20 orang siswa dan guru dengan nilai rata-rata 0,86 kategori efektif. Abstract This research is motivated by simulation learning and digital communication that the learning system still uses the lecture method. During the Covid-19 period it resulted in a reduction in learning hours from 45 minutes to 30 minutes, learning that took place online had an influence on teachers in delivering limited learning material and setbacks in terms of student understanding in class X at Gema Nusantara Vocational High School Bukittinggi. This study uses the development method or Research and Development (R&amp;D) with the Multimedia Development Life Cycle (MLDC) development model by Luther-Sutopo which consists of six stages, namely the Concept, Design, Material Collecting, Assembly, Testing, and Distribution stages. The results of this research are in the form of android-based learning media that can be used by teachers and students wherever and whenever they are. The results of the validity test were carried out by 3 experts, namely computer experts, Indonesian language and ICT materials with an average of 0.8 valid categories. The results of practicality by 3 examiners, namely Simulation and Digital Communication subject teachers at Gema Nusantara Vocational High School Bukittinggi, education lecturers at IAIN Bukittinggi and Computer lecturers at IAIN Bukittinggi with an average score of 0.91 in the practical category. Effectiveness test by 20 students and teachers with an average score of 0.86 effective category.
PORTABLE SMART MINI PATIENT MONITOR FOR IMPROVED HEALTHCARE OUTCOMES: PATIENT-BASED CARE DEVICE

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This study aims to present the prototype for Portable Smart Mini Patient Monitor for improved healthcare outcomes and patient-based care that records and displays the vital physiological signs including heartbeats, body temperature, ECG, and SPO2. The device has designed with multiple sensors connected with Arduino UNO and interfaced with an LCD display that provide real-time acquisition of the patient’s data. The recent era of medical advancement and digitization has embarked on the need for efficient and innovative medical devices that provide better admittance to competent and dynamic patient care like the smart mini patient monitor to facilitate substantial diagnosis and treatment of patients’ medical diseases by accurately identifying the pattern and effect of the ailment along with the improved patient experience and healthcare outcomes. The research aims to provide better healthcare opportunities to the patients on daily bases and improve the overall healthcare delivery system and make it work simultaneously with the increased population yearning for medical attention. The smart mini patient monitor was used for the detection of vital signs for five human subjects. The acquired results were then calibrated with a thermometer and BIOPAC device. The main idea of the study dwells upon the incorporation of multiple sensors into one portable device that yields multiple physiological signals at one time with utmost precision and can be easily tracked, stored, and&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; used for medical diagnosis.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Revisiting Scene Text Recognition: A Data Perspective

This paper aims to re-assess scene text recognition (STR) from a data-oriented perspective. We begin by revisiting the six commonly used benchmarks in STR and observe a trend of performance saturation, whereby only 2.91% of the benchmark images cannot be accurately recognized by an ensemble of 13 representative models. While these results are impressive and suggest that STR could be considered solved, however, we argue that this is primarily due to the less challenging nature of the common benchmarks, thus concealing the underlying issues that STR faces. To this end, we consolidate a large-scale real STR dataset, namely Union14M, which comprises 4 million labeled images and 10 million unlabeled images, to assess the performance of STR models in more complex real-world scenarios. Our experiments demonstrate that the 13 models can only achieve an average accuracy of 66.53% on the 4 million labeled images, indicating that STR still faces numerous challenges in the real world. By analyzing the error patterns of the 13 models, we identify seven open challenges in STR and develop a challenge-driven benchmark consisting of eight distinct subsets to facilitate further progress in the field. Our exploration demonstrates that STR is far from being solved and leveraging data may be a promising solution. In this regard, we find that utilizing the 10 million unlabeled images through self-supervised pre-training can significantly improve the robustness of STR model in real-world scenarios and leads to state-of-the-art performance.
OPHAvatars: One-shot Photo-realistic Head Avatars

We propose a method for synthesizing photo-realistic digital avatars from only one portrait as the reference. Given a portrait, our method synthesizes a coarse talking head video using driving keypoints features. And with the coarse video, our method synthesizes a coarse talking head avatar with a deforming neural radiance field. With rendered images of the coarse avatar, our method updates the low-quality images with a blind face restoration model. With updated images, we retrain the avatar for higher quality. After several iterations, our method can synthesize a photo-realistic animatable 3D neural head avatar. The motivation of our method is deformable neural radiance field can eliminate the unnatural distortion caused by the image2video method. Our method outperforms state-of-the-art methods in quantitative and qualitative studies on various subjects.
Root Extraction in Certain Finite Abelian $p$-Groups

We formulate a problem called \emph{Generalized Root Extraction} in finite Abelian groups that have more than one generator. We then study this problem for the specific case of the torsion subgroups of elliptic curves. We give a necessary and sufficient condition for the existence of a solution. We also present an algorithm to find a solution. Our algorithm easily generalizes to Abelian groups of prime power order that have a specific structure. We then discuss a variant of this problem called \emph{Simultaneous Root Extraction} and present an algorithm for solving it.
Can Model Fusing Help Transformers in Long Document Classification? An Empirical Study

Text classification is an area of research which has been studied over the years in Natural Language Processing (NLP). Adapting NLP to multiple domains has introduced many new challenges for text classification and one of them is long document classification. While state-of-the-art transformer models provide excellent results in text classification, most of them have limitations in the maximum sequence length of the input sequence. The majority of the transformer models are limited to 512 tokens, and therefore, they struggle with long document classification problems. In this research, we explore on employing Model Fusing for long document classification while comparing the results with well-known BERT and Longformer architectures.
New Security Proof of a Restricted High-Dimensional QKD Protocol

High-dimensional (HD) states are known to have several interesting properties when applied to quantum cryptography. For quantum key distribution (QKD), these states have the potential to improve noise tolerance and efficiency. However, creating, and measuring, HD states is technologically challenging, thus making it important to study HD-QKD protocols where Alice and Bob are restricted in their quantum capabilities. In this paper, we revisit a particular HD-QKD protocol, introduced in (PRA 97 (4):042347, 2018), which does not require Alice and Bob to be capable of sending and measuring in full mutually unbiased bases. In a way, the protocol is a HD version of the three state BB84: one full basis is used for key distillation, but only a single state is used, from an alternative basis, for testing the fidelity of the channel. The previous proof of security for this protocol has relied on numerical methods, making it difficult to evaluate for high dimensions. In this work, we provide a new proof of security, and give an explicit key-rate equation for depolarization channels, allowing us to evaluate the key-rate for arbitrarily high dimensional states. Furthermore, our new proof produces better results than prior work for dimensions greater than eight, and shows that HD-states can benefit restricted protocols of this nature.
Can Neural Network Memorization Be Localized?

Recent efforts at explaining the interplay of memorization and generalization in deep overparametrized networks have posited that neural networks $\textit{memorize}$ "hard" examples in the final few layers of the model. Memorization refers to the ability to correctly predict on $\textit{atypical}$ examples of the training set. In this work, we show that rather than being confined to individual layers, memorization is a phenomenon confined to a small set of neurons in various layers of the model. First, via three experimental sources of converging evidence, we find that most layers are redundant for the memorization of examples and the layers that contribute to example memorization are, in general, not the final layers. The three sources are $\textit{gradient accounting}$ (measuring the contribution to the gradient norms from memorized and clean examples), $\textit{layer rewinding}$ (replacing specific model weights of a converged model with previous training checkpoints), and $\textit{retraining}$ (training rewound layers only on clean examples). Second, we ask a more generic question: can memorization be localized $\textit{anywhere}$ in a model? We discover that memorization is often confined to a small number of neurons or channels (around 5) of the model. Based on these insights we propose a new form of dropout -- $\textit{example-tied dropout}$ that enables us to direct the memorization of examples to an apriori determined set of neurons. By dropping out these neurons, we are able to reduce the accuracy on memorized examples from $100\%\to3\%$, while also reducing the generalization gap.
Towards a Rosetta Stone for (meta)data: Learning from natural language to improve semantic and cognitive interoperability

In order to effectively manage the overwhelming influx of data, it is crucial to ensure that data is findable, accessible, interoperable, and reusable (FAIR). While ontologies and knowledge graphs have been employed to enhance FAIRness, challenges remain regarding semantic and cognitive interoperability. We explore how English facilitates reliable communication of terms and statements, and transfer our findings to a framework of ontologies and knowledge graphs, while treating terms and statements as minimal information units. We categorize statement types based on their predicates, recognizing the limitations of modeling non-binary predicates with multiple triples, which negatively impacts interoperability. Terms are associated with different frames of reference, and different operations require different schemata. Term mappings and schema crosswalks are therefore vital for semantic interoperability. We propose a machine-actionable Rosetta Stone Framework for (meta)data, which uses reference terms and schemata as an interlingua to minimize mappings and crosswalks. Modeling statements rather than a human-independent reality ensures cognitive familiarity and thus better interoperability of data structures. We extend this Rosetta modeling paradigm to reference schemata, resulting in simple schemata with a consistent structure across statement types, empowering domain experts to create their own schemata using the Rosetta Editor, without requiring knowledge of semantics. The Editor also allows specifying textual and graphical display templates for each schema, delivering human-readable data representations alongside machine-actionable data structures. The Rosetta Query Builder derives queries based on completed input forms and the information from corresponding reference schemata. This work sets the conceptual ground for the Rosetta Stone Framework that we plan to develop in the future.
Revealing the Predictive Power of Neural Operators for Strain Evolution in Digital Composites

The demand for high-performance materials, along with advanced synthesis technologies such as additive manufacturing and 3D printing, has spurred the development of hierarchical composites with superior properties. However, computational modelling of such composites using physics-based solvers, while enabling the discovery of optimal microstructures, have prohibitively high computational cost hindering their practical application. To this extent, we show that Neural Operators (NOs) can be used to learn and predict the strain evolution in 2D digital composites. Specifically, we consider three architectures, namely, Fourier NO (FNO), Wavelet NO (WNO), and Multi-wavelet NO (MWT). We demonstrate that by providing a few initial strain frames as input, NOs can accurately predict multiple future time steps in an extremely data-efficient fashion, especially WNO. Further, once trained, NOs forecast the strain trajectories for completely unseen boundary conditions. Among NOs, only FNO offers super-resolution capabilities for estimating strains at multiple length scales, which can provide higher material and pixel-wise resolution. We also show that NOs can generalize to arbitrary geometries with finer domain resolution without the need for additional training. Based on all the results presented, we note that the FNO exhibits the best performance among the NOs, while also giving minimum inference time that is almost three orders magnitude lower than the conventional finite element solutions. Thus, FNOs can be used as a surrogate for accelerated simulation of the strain evolution in complex microstructures toward designing the next composite materials.
Noor-Ghateh: A Benchmark Dataset for Evaluating Arabic Word Segmenters in Hadith Domain

There are many complex and rich morphological subtleties in the Arabic language, which are very useful when analyzing traditional Arabic texts, especially in the historical and religious contexts, and help in understanding the meaning of the texts. Vocabulary separation means separating the word into different parts such as root and affix. In the morphological datasets, the variety of labels and the number of data samples helps to evaluate the morphological methods. In this paper, we present a benchmark data set for evaluating the methods of separating Arabic words which include about 223,690 words from the book of Sharia alIslam, which have been labeled by experts. In terms of the volume and variety of words, this dataset is superior to other existing data sets, and as far as we know, there are no Arabic Hadith Domain texts. To evaluate the dataset, we applied different methods such as Farasa, Camel, Madamira, and ALP to the dataset and we reported the annotation quality through four evaluation methods.
Traffic-Domain Video Question Answering with Automatic Captioning

Video Question Answering (VidQA) exhibits remarkable potential in facilitating advanced machine reasoning capabilities within the domains of Intelligent Traffic Monitoring and Intelligent Transportation Systems. Nevertheless, the integration of urban traffic scene knowledge into VidQA systems has received limited attention in previous research endeavors. In this work, we present a novel approach termed Traffic-domain Video Question Answering with Automatic Captioning (TRIVIA), which serves as a weak-supervision technique for infusing traffic-domain knowledge into large video-language models. Empirical findings obtained from the SUTD-TrafficQA task highlight the substantial enhancements achieved by TRIVIA, elevating the accuracy of representative video-language models by a remarkable 6.5 points (19.88%) compared to baseline settings. This pioneering methodology holds great promise for driving advancements in the field, inspiring researchers and practitioners alike to unlock the full potential of emerging video-language models in traffic-related applications.
Application of BadNets in Spam Filters

Spam filters are a crucial component of modern email systems, as they help to protect users from unwanted and potentially harmful emails. However, the effectiveness of these filters is dependent on the quality of the machine learning models that power them. In this paper, we design backdoor attacks in the domain of spam filtering. By demonstrating the potential vulnerabilities in the machine learning model supply chain, we highlight the need for careful consideration and evaluation of the models used in spam filters. Our results show that the backdoor attacks can be effectively used to identify vulnerabilities in spam filters and suggest the need for ongoing monitoring and improvement in this area.