Reviewerly/exHarmony huggingface dataset:
Efficient Circuits for Permuting and Mapping Packed Values Across Leveled Homomorphic Ciphertexts

Cloud services are an essential part of our digital infrastructure as organizations outsource large amounts of data storage and computations. While organizations typically keep sensitive data in encrypted form at rest, they decrypt it when performing computations, leaving the cloud provider free to observe the data. Unfortunately, access to raw data creates privacy risks. To alleviate these risks, researchers have developed secure outsourced data processing techniques. Such techniques enable cloud services that keep sensitive data encrypted, even during computations. For this purpose, fully homomorphic encryption is particularly promising, but operations on ciphertexts are computationally demanding. Therefore, modern fully homomorphic cryptosystems use packing techniques to store and process multiple values within a single ciphertext. However, a problem arises when packed data in one ciphertext does not align with another. For this reason, we propose a method to construct circuits that perform arbitrary permutations and mappings of such packed values. Unlike existing work, our method supports moving values across multiple ciphertexts, considering that the values in real-world scenarios cannot all be packed within a single ciphertext. We compare our open-source implementation against the state-of-the-art method implemented in HElib, which we adjusted to work with multiple ciphertexts. When data is spread among five or more ciphertexts, our method outperforms the existing method by more than an order of magnitude. Even when we only consider a permutation within a single ciphertext, our method still outperforms the state-of-the-art works implemented by HElib for circuits of similar depth.
Training Model of Basketball Offensive Route Based on Nonlinear Differential Equation

Abstract We study the trajectory of basketball based on nonlinear differential equations. At the same time, we design the optimal plan for shooting. We discuss the effect of the distance between the center of the ball and the center of the basket when shooting, the height of the center of the ball, and the change in the speed of the ball on the ball hit rate. The article comprehensively solves its motion trajectory. At the same time, we use the meaning of derivatives to find the maximum value of variables such as required height and speed. Research to find the best shooting spot and shooting angle.
CSGAN: Synthesis-Aided Brain MRI Segmentation on 6-Month Infants

Due to the early myelination process in infant brain development, white matter (WM) tissue demonstrates isointense with gray matter (GM) tissue in brain MRI for infants around 6 months. This process results in extremely low brain tissue intensity contrast between WM and GM in MRI, thus leading to a great challenge for experts to manually delineate brain tissue boundaries. Hence, for brain tissue segmentation task of 6-month-old infant MRI, the number of training data with reliable labels is quite limited. Inspired by the recent progress in the image synthesis field, we propose to use the largely available 12-month brain MR data and their relatively reliable segmentation masks to synthesize 6-month-like brain MR data with corresponding segmentation masks. Briefly, we design a novel model called CSGAN for 6-month MR image synthesis. CSGAN introduces a spatially-adaptive normalization (SPADE) module in the generators of CycleGAN, by embedding semantic information into networks to keep the brain anatomical structure consistent across 6-month and 12-month brain MRI. After that, we train an initial segmentation model on these augmented data to overcome the isointense problem in 6-months infant MRI. In experiments, the segmentation model is further fine-tuned on the real 6-month MR images from the public dataset iSeg-2019, and the evaluation results demonstrate superior performance of our method compared to state-of-the-art studies.
Detection of Image Forgery Through Novel Hybrid Technique

The rapid development of image editing applications has brought about an immense number of doctored photos coursing in our day by day lives, encouraging an exorbitant interest for automated forgery detection algorithms that can rapidly check the legitimacy of the source image. A robust forgery detection system needs to process the images without the need for prior knowledge about the image or any integral watermark. Forgery detection systems can be developed based on several indicators extracted using the image production approach and by analysing the doctored photos for any abnormal behaviours when compared to the original image. Digital images are considered to portray visual information in the current era and have become quiet predominant in our day-to-day life. Digital images are predominantly used in several sectors for analysing purpose. Few of these sectors include clinical science, criminal assessment, etc., where the legitimacy of an image plays a crucial role. Prediction of forgery in digital images is considered to be the most crucial part of any investigation. In the subject of digital image fraud identification, adequate quality work has depleted the first decade. Detection of forgeries in a digital image and the application standard tools seems to be more optimal with respect to time, space, and ease of work. The methodology proposed over here applies a hybrid combination of deep learning (DL) and machine learning. The proposed hybrid combinations segregate the image as forged and non-forged images. The proposed approach also explores the application and effectiveness of neural network to predict forgery images.
A Two-Stage Convolutional Neural Network for Hand Gesture Recognition

Gesture recognition task is a challenging research topic due to different hand sizes and poses. A hand gesture recognition convolutional residual network that learns to partition the hand region from the background is proposed. The second stage employs two convolutional neural networks which takes appearance information and the shape information as input to perform the gesture recognition. The appearance information is given by RGB images, and shape information is given by segmented gesture region obtained from first stage. The classification is computed based on accuracy, precision, and F1-score. The results manifest a high accuracy of 98.75%, F-score of 0.94, recall of 0.94, and a precision of 0.95 which is better than other methods on OUHANDS dataset.
Song authorship attribution: a lyrics and rhyme based approach

Abstract In this work, we apply authorship attribution to a large-scale corpus of song lyrics. As a sub-category of poetry, song lyrics embody cultural elements as well as stylistic attributes that are not present in prose. We draw attention to special characteristics such as repetitive sound patterns and rhyme based structures in lyrics that can be key to ownership, and present opportunities that cannot be employed for authorship attribution of other types of text such as tweets, emails, and blog posts. We first create a new balanced, large-scale data set of 12,000 song lyrics from 120 different artists. We propose CNN models for authorship attribution on this song lyric data set, in order to use structural information included in the lyrics, similarly to image classification. We conduct experiments at the character and sub-word levels that mostly reflect positional information. In addition, we use phoneme level features, which intrinsically involve attributes such as repetitions, rhyme, and meter, and represent elements unique to verse-based textual compositions. We attempt to discover idiosyncratic features and consequently author and genre associations by working with variants of CNN architectures that have been successfully used in other text classification domains. Our architecture choice results in a particular focus on lyric attributes residing in neighboring regions, since CNNs fail to apprehend long term textual dependencies. Finally, we empirically evaluate our results in comparison with the findings of previous test classification research from different domains.
Akut Lenfositik Löseminin Makine Öğrenimi Yöntemleriyle Otomatik Tespitine İlişkin Karşılaştırmalı Bir Çalışma

Acute Lymphocytic Leukemia (ALL) is one of the most prevalent types of leukemia which has the risk of death of children is relatively higher than adults. The early diagnosis of this disease is crucial and it can be detected by examining the morphological changes of the blood cells. In this study, we exhibit a comparative study on the automatic classification and identification of the ALL with machine learning methodologies. Acute Lymphoblastic Challange Database (ALL-CDB) served by the Cancer Imaging Archive, which consists of 6500 digital microscopic pathology images from 118 subjects, is used. As the first step, the geometric features are extracted and after, the feature selection was performed with Principal Component Analysis (PCA). Finally, the classification process on the selected features was carried out by using Naive Bayes, k-Nearest Neighbor (k-NN), Linear Discriminant Analysis (LDA), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) neural network methods. The results between the methodologies have been analyzed in terms of accuracy, precision, recall, and F1-score metrics. According to the results, MLP gives the both highest accuracy and F1-score with 97% to classify the ALL cells for leukemia.
Dialogue Topic Extraction as Sentence Sequence Labeling

The topic information of the dialogue text is important for the model to understand the intentions of the dialogue participants and to abstractly summarize the content of the dialogue. The dialogue topic extraction task aims to extract the evolving topic information in long dialogue texts. In this work, we focus on topic extraction of dialogue texts in customer service scenarios. Based on the rich sequence features in the topic tags, we define this task as a sequence labeling task with sentences as the basic elements. For this task, we build a dialogue topic extraction system using a Chinese pre-trained language model and a CRF model. In addition, we use sliding windows to avoid excessive loss of contextual information, and use adversarial training and model integration to improve the performance and robustness of our model. Our system ranks first on the track 1 of the NLPCC-2022 shared task on Dialogue Text Analysis, Topic Extraction and Dialogue Summary.
3D Animation Simulation of Computer Fractal and Fractal Technology Combined with Diamond-Square Algorithm

Abstract This article studies the generation of 3D animation simulation based on gray value algorithm and fractal interpolation algorithm. The article uses fractal technology combined with the Diamond-Square algorithm to generate height data and then color it. This algorithm optimizes the 3D animation process. The results show that the algorithm is fast in generating speed and only needs to input a few simple animation parameters to generate different 3D animations.
Protecting White-Box Block Ciphers with Galois/Counter Mode

All along, white-box cryptography researchers focus on the design and implementation of certain primitives but less to the practice of the cipher working modes. For example, the Galois/Counter Mode (GCM) requires block ciphers to perform only the encrypting operations, which inevitably facing code-lifting attacks under the white-box security model. In this paper, a code-lifting resisted GCM (which is named WBGCM) is proposed to mitigate this security drawbacks in the white-box context. The basic idea is to combining external encodings with exclusive-or operations in GCM, and therefore two different schemes are designed with external encodings (WBGCM-EE) and maskings (WBGCM-Maksing), respectively. Furthermore, WBGCM is instantiated with Chow et al.'s white-box AES, and the experiments show that the processing speeds of WBGCM-EE and WBGCM-Masking achieves about 5 MBytes/Second with a marginal storage overhead.
Intelligent measurement and detection data analysis and application

In measuring technique is widely used, the measurement test data acquisition, processing, management is the most common operation link, because each link related to data information is relatively more, practice is more complex, so in order to ensure the accuracy of the calibration data and results, the current research scholars use intelligent technology to build a new platform for the collection and management system, It can effectively solve the problems existing in intelligent metering detection. Therefore, on the basis of understanding the current situation of measurement detection data collection and intelligent management, I deeply discuss how to use intelligent measurement detection reasonably according to the measurement detection management system of mobile Internet. The final results show that the intelligent measurement detection technology system can achieve comprehensive control of basic functions on the basis of obtaining more data information.
Design and implementation of college education resource sharing platform based on IPFS+ block chain dual database

This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications. The best is to read these instructions and follow the outline of this text. Please make the page settings of your word processor to A4 format (21 x 29,7 cm or 8 x 11 inches); with the margins: bottom 1.5 cm (0.59 in) and top 3 cm (1.18 in), right/left margins must be 2 cm (0.78 in). This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications. The best is to read these instructions and follow the outline of this text. Please make the page settings of your word processor to A4 format (21 x 29,7 cm or 8 x 11 inches); with the margins: bottom 1.5 cm (0.59 in) and top 3 cm (1.18 in), right/left margins must be 2 cm (0.78 in).
Towards Neural Routing with Verified Bounds on Performance

When data-driven algorithms, especially the ones based on deep neural networks (DNNs), replace classical ones, their superior performance often comes with difficulty in their analysis. On the way to compensate for this drawback, formal verification techniques, which can provide reliable guarantees on program behavior, were developed for DNNs. These techniques, however, usually consider DNNs alone, excluding real-world environments in which they operate, and the applicability of techniques that do account for such environments is often limited. In this work, we consider the problem of formally verifying a neural controller for the routing problem in a conveyor network. Unlike in known problem statements, our DNNs are executed in a distributed context, and the performance of the routing algorithm, which we measure as the mean delivery time, depends on multiple executions of these DNNs. Under several assumptions, we reduce the problem to a number of DNN output reachability problems, which can be solved with existing tools. Our experiments indicate that sound-and-complete formal verification in such cases is feasible, although it is notably slower than the gradient-based search of adversarial examples.The paper is structured as follows. Section 1 introduces basic concepts. Then, Section 2 introduces the routing problem and DQN-Routing, the DNN-based algorithm that solves it. Section 3 proposes the contribution of this paper: a novel sound and complete approach to formally check an upper bound on the mean delivery time of DNN-based routing. This approach is experimentally evaluated in Section 4. The paper is concluded with some discussion of the results and outline of possible future work.
Classification of Articles from Mass Media by Categories and Relevance of the Subject Area

The research is devoted to classification of news articles about P. G. Demidov Yaroslavl State University (YarSU) into 4 categories: “society”, “education”, “science and technologies”, “not relevant”.The proposed approaches are based on using the BERT neural network and methods of machine learning: SVM, Logistic Regression, K-Neighbors, Random Forest, in combination of different embedding types: Word2Vec, FastText, TF-IDF, GPT-3. Also approaches of text preprocessing are considered to achieve higher quality of the classification. The experiments showed that the SVM classifier with TF-IDF embedding and trained on full article texts with titles achieved the best result. Its micro-F-measure and macro-F-measure are 0.8214 and 0.8308 respectively. The BERT neural network trained on fragments of paragraphs with YarSU mentions, from which the first 128 words and the last 384 words were taken, showed comparable results. The resulting micro-F-measure and macro-F-measure are 0.8304 and 0.8181 respectively. Thus, using paragraphs with the target organisation mentions is enough to classify text by categories efficiently.
Epidemic Prevention and Control System of Rail Transit Station

With the normalization of the epidemic situation, rail transit, as an important way of travel for urban residents, the station space is relatively closed, resulting in great pressure on epidemic prevention and control. Therefore, it is necessary to study the epidemic prevention and control system of rail transit stations and use emerging technologies to improve the efficiency of epidemic prevention and control. Based on the requirements of station epidemic control, this paper puts forward the system structure, overall architecture and key technologies of epidemic control subsystem, and summarizes that the epidemic control system of rail transit station has the advantages of high security, strong scalability and high sharing ability, which is conducive to improving the efficiency of station epidemic control.
Research on Transferable Characteristics of Adversarial Examples Generated Based on Gradient Information

Deep neural networks are easily disturbed by adversarial examples, which bring many security problems to the application of artificial intelligence technology. Adversarial examples and adversarial attacks can be useful tools for evaluating the robustness of deep learning models before they are deployed. However, most of the adversarial examples generated by a single network model have weak transferability, which can only have a good attack effect on the network model, and it is difficult to successfully attack other network models. In response to this problem, this paper studies the adversarial examples generated by the Fast Gradient Sign Method (FGSM) and the Basic Iterative Method (BIM) and their transferability on the ImageNet dataset. Meantime, the paper proposes a pixel-level image fusion method to enhance the transferability. The adversarial examples generated by the method can be better applied to attack a variety of neural network models. These adversarial examples with strong transferability can be used as a benchmark to measure the robustness of DNN models and defense methods.
Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency

Sample efficiency is crucial for imitation learning methods to be applicable in real-world applications. Many studies improve sample efficiency by extending adversarial imitation to be off-policy regardless of the fact that these off-policy extensions could either change the original objective or involve complicated optimization. We revisit the foundation of adversarial imitation and propose an off-policy sample efficient approach that requires no adversarial training or min-max optimization. Our formulation capitalizes on two key insights: (1) the similarity between the Bellman equation and the stationary state-action distribution equation allows us to derive a novel temporal difference (TD) learning approach; and (2) the use of a deterministic policy simplifies the TD learning. Combined, these insights yield a practical algorithm, Deterministic and Discriminative Imitation (D2-Imitation), which operates by first partitioning samples into two replay buffers and then learning a deterministic policy via off-policy reinforcement learning. Our empirical results show that D2-Imitation is effective in achieving good sample efficiency, outperforming several off-policy extension approaches of adversarial imitation on many control tasks.
A survey of exercise beliefs among people with Parkinson’s disease in Malaysia

Introduction A substantial amount of evidence supports exercise as crucial non-pharmacological management to slow down the progression of Parkinson’s disease. Despite that, people with Parkinson’s disease are still known to be physically inactive. Hence, there is a need to investigate the exercise beliefs and participation benefits so that strategies can be developed to improve their exercise involvement. Methods Overall, 47 respondents from a non-governmental organization who met the inclusion criteria were recruited purposively to determine their beliefs concerning exercise. A cross-sectional survey was conducted with the use of a self-administered and validated questionnaire. Results A significant proportion of the participants (80.9%) believed that exercise could help slow down the progression of Parkinson’s disease; 17% were not sure and only 2.1% answered that exercise did not slow down the disease progression. The majority of the subjects (68%) were more likely to believe that exercise improved balance. Pertaining to the belief in physiotherapy for people with Parkinson’s disease, 76.6% answered yes, 17% were not sure, and 6.4% said that they did not believe in physiotherapy for people with Parkinson’s disease. Conclusions The study revealed that most participants believed in exercise and were aware of its usefulness for their condition. However, they also reported that exercise and physiotherapy were not routinely recommended to them. So, still, awareness is needed of the exercise role and participation benefits.
Context-Based Vulnerability Risk Scoring and Prioritization

Protecting an organization’s intellectual property, financial secrets, and performance is crucial because it is sensitive data that if compromised could be catastrophic to the organization in question. As a result of the growing economy, organizations of scale have a significant portion of their infrastructure over technology which makes the organization vulnerable. The security teams of such organizations work to patch such vulnerabilities as they come across them but may spend a significant amount of organization resources fixing vulnerabilities that may not be exploited. After conducting our own research on the existing methods to prioritize vulnerabilities that have a higher probability of being exploited, we found that machine learning can be used to make the process of vulnerability prioritization efficient. This paper discusses our research on using machine learning for vulnerability prioritization and the different machine learning algorithms that can be of use for the same. This paper also discusses our approach on creating a system for vulnerability prioritization in an organization.