Reviewerly/exHarmony huggingface dataset:
RCAE_BFV: Retrieve Encrypted Images using Convolution AutoEncoder and BFV

Content-Based Image Retrieval (CBIR) is an actual application in computer vision, which retrieves similar images from a database. Deep Learning (DL) is essential in many applications, including image retrieval applications. However, encryption techniques are used to protect data privacy because these data are vulnerable to being viewed by unauthorized parties while being transmitted over unsecured channels. This paper includes two parts for images retrieval. In the first part, features of all images of a Canadian Institute for Advanced Research CIFAR-10 dataset were extracted and stored on the Server-side. In the second part, the Brakerski/Fan-Vercauteren (BFV) homomorphic encryption scheme method for encrypting an image sent by the client-side. First, their decryption and image features are extracted depending on the trainer model when they arrive on the server-side. Then an extracted features are compared with stored features using the Cosine Distance method, and then the server encrypts the retrieved images and sends them to the client-side. Deep-learning results on plain images were 97% for classification and 96.7% for retriever images. At the same time, The National Institute of Standards and Technology (NIST ) test was used to check the security of BFV when applied to CIFAR-10 dataset. Index Terms— BFV, Convolution Autoencoder, Content-based image retrieval, Homomorphic encryption.
Material variability compensation in a finite element model of a classical guitar by appropriate geometry modifications

The sound of some old master instruments like the Stradivari violins or the Torres guitars is still unrivaled for many musicians. The common practice among instrument makers is to copy these instruments by building instruments with the same geometry as the original. However, audible differences between these copies and the original instruments are unavoidable, mainly due to the natural variability of the wood characteristics. A suitable measure to objectively characterize an instrument's sound is evaluating its modal parameters. Our project aims to develop a methodology to design copies of the old master instruments that match these modal parameters of the original instruments instead of copying their geometry. After showing that such an approach is appropriate for an individual guitar soundboard in a recent publication, we now present a parameter study to find suitable geometric modifications on an entire guitar to compensate for the natural material variation. Therefore, this study is based on a detailed finite-element model that shall predict the effect of suitable geometry alterations on the instrument's modal parameters and suited optimization techniques. Geometric modifications like the shape of the braces, the thickness of the fretboard, and the thickness of the plates are taken into account.
Retraction: New classification scheme for autoclave security data sets using data mining patterns (J. Phys.: Conf. Ser. 1228 012013)

This article has been retracted by IOP Publishing following an allegation that this article may contain tortured phrases [1]. IOP Publishing has investigated and agrees the article contains a number of nonsensical phrases that feature throughout the paper [2], to the extent that the article makes very little sense. This casts serious doubt over the legitimacy of the article. IOP Publishing wishes to credit PubPeer commenters [3] for bringing the issue to our attention. The authors neither agree or disagree to this retraction. [1] Cabanac G, Labbe C, Magazinov A, 2021, Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals, arXiv:2107.06751v1. [2] Chalasani Srinivas et al 2019 J. Phys.: Conf. Ser. 1228 012013 [3] https://pubpeer.com/publications/5FD25A2C0C877388EC5C2380B77509 Retraction published: 27 April 2023
A Novel Machine learning Algorithms used to Detect Credit Card Fraud Transactions

During the Covid-19 pandemic, the world was under lockdown, and everyone was inside their home. There are so many restrictions for going out, so many companies introduced online shopping, and this online shopping helped more people; the e-commerce platform also increased their revenue; at the same time, online fraud has also risen worldwide. Everyone adopted online shopping during the pandemic. In 2019 India's 2019 credit/debit card fraud rate was 365, according to the National Crime Record Bureau. The developed countries are the highest rate of credit card fraud in 2020 compared to India; for that reason, we have to implement mechanisms that can detect credit theft. The machine learning algorithm with the R program will play an essential role in credit card fraud detection. The following machine learning algorithm will have used for credit card fraud, Random Forest, Logistic regression, Decision trees, and Gradient Boosting Classifiers. The European bank dataset used in our research and the dataset size is 284808. Here we used two classes, the first one is called the positive class (fraud transactions), and the second one is the negative class (genuine transactions). The final result will show us the outperforms of our proposed system.
An Adaptable Optimal Network Topology Model for Efficient Data Centre Design in Storage Area Networks

In this research, we look at how different network topologies affect the energy consumption of modular data centre (DC) setups. We use a combined-input directed approach to assess the benefits of rack-scale and pod-scale fragmentation across a variety of electrical, optoelectronic, and composite network architectures in comparison to a conventional DC. When the optical transport architecture is implemented and the appropriate resource components are distributed, the findings reveal fragmentation at the layer level is adequate, even compared to a pod-scale DC. Composable DCs can operate at peak efficiency because of the optical network topology. Logical separation of conventional DC servers across an optical network architecture is also investigated in this article. When compared to physical decentralisation at the rack size, logical decomposition of data centers inside each rack offers a small decrease in the overall DC energy usage thanks to better resource needs allocation. This allows for a flexible, composable architecture that can accommodate performance based in-memory applications. Moreover, we look at the state of fundamentalmodel and its use in both static and dynamic data centres. According to our findings, typical DCs become more energy efficient when workload modularity increases, although excessive resource use still exists. By enabling optimal resource use and energy savings, disaggregation and micro-services were able to reduce the typical DC's up to 30%. Furthermore, we offer a heuristic to duplicate the Mixed integer model's output trends for energy-efficient allocation of caseloads in modularized DCs.
Comparison of large-scale pre-trained models based ViT, swin transformer and ConvNeXt

In the field of computer vision, deep learning has developed tremendously, large-scale preforming has received increasing attention from experts and researchers. Different training models often have large performance gaps in training speed and accuracy when performing large-scale pre-training. In this case, choosing the appropriate model for large-scale pre-training is particularly important. This experiment uses the same image data set and the same hardware conditions to construct the image classification model respectively in the three mainstream image recognition large-scale pre-training models, Vision Transformer (VIT), Swin-Transformer and ConvNeXt, try to analyze the advantages and disadvantages of each model by experimental results. It is observed that Vision Transformer has the fastest running speed in computer vision classification experiments, but its accuracy is not as good as the other two models, Swin-Transformer has the slowest speed and average accuracy, ConvNeXt has the highest accuracy, but its speed is mediocre. The results of this experiment have some reference significance for future model selection for large-scale pre-training tasks in computer vision, this can decrease training time and improve training accuracy to some extent.
Dynamic gesture recognition based on temporal shift module

Dynamic gesture recognition is a very important interaction method in human-computer interaction. For the current research, multi-modal data and three-dimensional convolutional neural network are often used for training. Although the recognition accuracy is high and robustness is good, the amount of parameters is large and high computational cost. To solve the problem, a dynamic gesture recognition method based on Temporal Shift Module (TSM) is proposed on the basis of two-dimensional convolutional neural network. This method uses PyConvResNet-50 as the backbone network, adds the TSM module for information exchange in the time dimension, embeds the Motion Excitation module (ME) into the TSM to enhance short-term temporal modeling, and finally uses 2D-FCN for spatiotemporal feature fusion classification. The experimental results show that the recognition accuracy of the model on the large-scale gesture dataset Jester is 96.49%, which is comparable to that of the three-dimensional convolutional neural network, but the calculation amount is reduced by 63% as well. This method is suitable for the field of gesture recognition that requires high real-time performance.
Research on university network public opinion sentiment analysis based on BERT and Bi-LSTM

This paper proposes a method of emotion analysis based on BERT BiLSTM. Firstly, BERT is used to realize the word vectorization, and then Bilstm is constructed to extract semantic features for emotional analysis. In the experiment, the model designed in this paper is compared with the emotional dictionary, SVM, Word2vec LSTM, BERT TextCNN on the college online public opinion comment dataset, and the experiment proves that the accuracy of this model has been improved.
UAV obstacle avoidance based on improved artificial potential field method

The traditional artificial potential field method, distance is the only factor to determine the potential field force. When the UAV enters the obstacle's range of action, it is repelled by its potential field, the obstacle will have a repulsive effect on the UAV and as the distance continues to approach, the UAV is subjected to more and more repulsive force, making the UAV avoidance time is too long and the avoidance path is wasted. This paper proposes an improved artificial potential field method for the UAV forward path and obstacles do not intersect and is still in the variety of action of the repulsive potential field, which solves the problem that when the UAV forward direction does not intersect with the obstacles, the UAV is in the range of action of the repulsive potential field and is not subject to repulsive force, avoiding the waste of obstacle avoidance path. It is demonstrated through simulation analysis that the proposed obstacle avoidance algorithm produces superior results.
Lane detection algorithm based on multi-head self-attention and multi-level feature fusion

Lane detection is a crucial environmental sensing technique that is used in advanced driving assistance systems and automatic driving. The research on this issue has significant practical value. Aiming the current lane detection algorithm could not solve the problems of the local receptive field and detail feature loss, we introduced the multi-head self-attention module in Transformer into the encoder and decoder to obtain the global receptive field while solving the problem of detail feature loss with the multi-level feature fusion decoder. The proposed algorithm has been compared with the ERFNet model in the CULane dataset, and the detection accuracy has improved by 3.9 percentage points. The detection accuracy in the Tusimple dataset is 96.51%. Introducing a multi-head self-attention module increases the feature selection effect of the attention mechanism in the coding and decoding process. It provides a new solution for the lane detection algorithm.
PENGGUNAAN METODE PICTURE AND PICTURE UNTUK MENINGKATKAN KEMAMPUAN LITERASI BACA TULIS PADA SISWA KELAS 1 SEKOLAH DASAR

Competencies that must exist at this time are reliable human resources .namely human resources who have literate abilities ( reading and writing). In addition to literate abilities orations skills ( listening and speaking) are also needded,in which the two skills are an integral part of each other. Connected thas is used in everiday life . In everiday life human resources are competent as one to live life by digging up as much inforamtion as possible ao that they become human resources capable of contributing to personal and national progress because a nation will be said to develop if all human resources have awareness of literacy,because literacy will open doors of knowledge that have never been solved before. In that way literacy is the main key in a science.The purpose of this study was to find out and examine the process of apliyying the picture and picture to the learning of elemntary school students in grade 1.also the obstacles faced by teachers and students in carrying out learning using the picture and picture model.
Pattern design and implementation based on iterated function system

Aiming at virtual applications, a prototype of affine transformation for designing patterns is proposed in this paper. Due to the capability of generating fractal geometry with self-similar and micro-fine structure by simple affine transformation, the iterative function system (IFS) is utilized to model the prototype, which is comprised of prototype the affine transform, computing the parameters of IFS ,designing and implementing iterative algorithms, and generating basic fractal sets as well. Simulation experiments has shown the validity of the proposed method.
MediSage: An AI Assistant for Healthcare via Composition of Neural-Symbolic Reasoning Operators

We introduce MediSage, an AI decision support assistant for medical professionals and caregivers that simplifies the way in which they interact with different modalities of electronic health records (EHRs) through a conversational interface. It provides step-by-step reasoning support to an end-user to summarize patient health, predict patient outcomes and provide comprehensive and personalized healthcare recommendations. MediSage provides these reasoning capabilities by using a knowledge graph that combines general purpose clinical knowledge resources with recent-most information from the EHR data. By combining the structured representation of knowledge with the predictive power of neural models trained over both EHR and knowledge graph data, MediSage brings explainability by construction and represents a stepping stone into the future through further integration with biomedical language models.
Unsupervised feature selection regression model with nonnegative sparsity constraints

Selecting appropriate features can better describe the characteristics and structure of data, which play an important role in further improving models and algorithms whether for supervised or unsupervised learning. In this paper, a new unsupervised feature selection regression model with nonnegative sparse constraints (URNS) is proposed. The algorithm combines nonnegative orthogonal constraint, L2,1-norm minimum optimization and spectral clustering. Firstly, the linear regression model between the features and the pseudo labels is given, and the indicator matrix, which describes feature weight, is subject to nonnegative and orthogonal constraints to select better features. Secondly, in order to reduce redundant and even noisy features, L2,1-norm for indicator matrix is added to the regression model for exploring the correlation between pseudo labels and features by the row sparsity property of L2,1-norm. Finally, pseudo labels of all samples are established by spectral clustering. In order to solve the regression model efficiently and simply, the method of nonnegative matrix decomposition is used and the complexity of the given algorithm is analysed. Moreover, a large number of experiments and analyses have been carried out on several public datasets to verify the superiority of the given model.
HMT: A Hardware-centric Hybrid Bonsai Merkle Tree Algorithm for High-performance Authentication

The Bonsai Merkle tree (BMT) is a widely used tree structure for authentication of metadata such as encryption counters in a secure computing system. Common BMT algorithms were designed for traditional Von Neumann architectures with a software-centric implementation in mind and as such, they are predominantly recursive and sequential in nature. However, the modern heterogeneous computing platforms employing Field-Programmable Gate Array (FPGA) devices require concurrency-focused algorithms to fully utilize the versatility and parallel nature of such systems. The recursive nature of traditional BMT algorithms makes them challenging to implement in such hardware-based setups. Our goal for this work is to introduce HMT, a hardware-friendly BMT algorithm that enables the verification and update processes to function independently and provides the benefits of relaxed update while being comparable to the eager update in terms of update complexity. The methodology of HMT contributes both novel algorithmic revisions and innovative hardware techniques to implementing BMT. We mathematically demonstrate the challenges of potentially unbounded recursions in relaxed BMT updates. To solve this problem, we use a partitioned BMT caching scheme that allocates a separate write-back cache for each BMT level—thus allowing for low and fixed upper bounds for dirty evictions compared to the traditional BMT caches. Then we introduce the aforementioned hybrid BMT algorithm that is hardware-targeted, parallel, and relaxes the update depending on BMT cache hit but makes the update conditions more flexible compared to lazy update to save additional write-backs. Deploying this new algorithm, we have designed a new BMT controller with a dataflow architecture including speculative buffers and parallel write-back engines to facilitate performance-enhancing mechanisms (like multiple concurrent authentication and independent updates) that were not possible with the conventional lazy algorithm. Our empirical performance measurements on a Xilinx U200 accelerator FPGA have demonstrated that HMT can achieve up to 7× improvement in bandwidth and 4.5× reduction in latency over lazy-update BMT baseline and up to 14% faster execution in standard benchmarks compared to a state-of-the-art, eager-update BMT solution.
An Ontological Approach for Unlocking the Colonial Archive

Cultural Heritage institutions have been exploring new ways of making available their catalogues in digital format. Recently, new approaches have emerged as methods to reuse and make available the contents for computational purposes. This work introduces a methodology to transform digital collections into Linked Open Data following best practices. The framework has been applied to Indigenous and Spanish colonial archives based on the collection Relaciones Geográficas of Mexico and Guatemala provided by the LLILAS Benson Latin American Studies and Collections. The results of this work are publicly available. This work aims at encouraging Cultural Heritage institutions to publish and reuse their digital collections using advanced methods and techniques.
A Research on Information Security Based on Digital Watermarking

Researchers in the fields of information security and computer image processing are deeply concerned about digital watermarking technology as piracy of digital products like images, audio, video, and files, which are becoming increasingly serious due to the Internet's rapid development and the media resources' digitalization.By detecting the watermark information embedded in the digital media to identify the media copyright, the copyright of the digital media can be effectively protected.In fact, digital watermarking has important application value in covert communication, bill anti-counterfeiting, digital signature, fingerprint identification, logo implicit authentication, and so on.In this paper, an algorithm-and algorithm-combination-based dual digital watermarking design technique is proposed.Based on a careful examination of the algorithms and algorithms one at a time, this creative proposal combines the two algorithms to achieve efficient embedding and extraction of digital watermarks and to take advantage of the double watermark's security benefits to encrypt the transmission of digital maps, thereby enhancing the security of image transmission.Then, the scheme is tested to prove its feasibility.At the same time, in order to verify the advantages of the scheme, a variety of attack experiments are carried out on the scheme, and the experimental data in the same environment are compared with other dual digital watermarking schemes.Finally, the superiority and robustness of the scheme are obtained, and it is applied and popularized in the military.The emphasis of this paper is to realize the information security of armored mechanized troops.During the battle, armored mechanized units use the positioning system equipped by armored vehicles to snap the geographical environment of the battlefield instantly, and upload the image to the rear combat command center in time through the network, military network, and people's network.In this long-distance transmission process, in order to ensure the security and timeliness of the transmitted images, it is necessary to use encryption technology to protect and verify the military images.
Meta-Generator Enhanced Multi-Domain Recommendation

Large-scale e-commercial platforms usually contain multiple business fields, which require industrial algorithms to characterize user intents across multiple domains. Numerous efforts have been made in user multi-domain intent modeling to achieve state-of-the-art performance. However, existing methods mainly focus on the domains having rich user information, which makes implementation to domains with sparse or rare user behavior meet with mixed success. Hence, in this paper, we propose a novel method named Meta-generator enhanced multi-Domain model (MetaDomain) to address the above issue. MetaDomain mainly includes two steps, 1) users’ multi-domain intent representation and 2) users’ multi-domain intent fusion. Specifically, in users’ multi-domain intent representation, we use the gradient information from a domain intent extractor to train the domain intent meta-generator, where the domain intent extractor has the input of users’ sequence feature and domain meta-generator has the input of users’ basic feature, hence the capability of generating users’ intent with sparse behavior. Afterward, in users’ multi-domain intent fusion, a domain graph is used to represent the high-order multi-domain connectivity. Extensive experiments have been carried out under a real-world industrial platform named Meituan. Both offline and rigorous online A/B tests under the billion-level data scale demonstrate the superiority of the proposed MetaDomain method over the state-of-the-art baselines. Furthermore comparing with the method using multi-domain sequence features, MetaDomain can reduce the serving latency by 20%. Currently, MetaDomain has been deployed in Meituan one of the largest worldwide Online-to-Offline(O2O) platforms.