Reviewerly/exHarmony huggingface dataset:
A progressive image semantic segmentation method using recurrent neural network

To improve the accuracy of image detail structure segmentation is a challenging research topic. In this paper, a progressively refined image semantics segmentation method based on recurrent neural network is presented. By introducing a recurrent processing layer into the neural network, a recurrent network structure is constructed, which makes the image segmentation from coarse to fine. Using the coarse-grained segmentation result of the previous segmentation module, it provides an effective context clue for the next fine-grained segmentation, and achieves progressive, multi-grained semantic image segmentation from coarse to fine. Comparative experiments on several datasets show that: image semantic segmentation based on recurrent neural network can effectively utilize the image context information and improve the accuracy of fine-grained image segmentation.
Shorter Lattice-Based Zero-Knowledge Proofs for the Correctness of a Shuffle

In an electronic voting procedure, mixing networks are used to ensure anonymity of the casted votes. Each node of the network re-encrypts the input list of ciphertexts and randomly permutes it in a process named shuffle, and must prove (in zero-knowledge) that the process was applied honestly. To maintain security of such a process in a post-quantum scenario, new proofs are based on different mathematical assumptions, such as lattice-based problems. Nonetheless, the best lattice-based protocols to ensure verifiable shuffling have linear communication complexity on N, the number of shuffled ciphertexts.In this paper we propose the first sub-linear (on N) post-quantum zero-knowledge argument for the correctness of a shuffle, for which we have mainly used two ideas: arithmetic circuit satisfiability results from [] and Bene\(\check{\text {s}}\) networks to model a permutation of N elements. The achieved communication complexity of our protocol with respect to N is \(\mathcal {O}(\sqrt{N}\log ^2(N))\), but we will also highlight its dependency on other important parameters of the underlying lattice ingredients.
On the Computational Complexity of Deep Learning Algorithms

The paper analyzes current research and the state of the industry to assess the complexity of machine learning algorithms. The tasks of deep learning are associated with an extremely high degree of computational complexity, which requires the use, first of all, of new algorithmic methods and an understanding of the assessment of the complexity of the calculations. This area of research is not given due attention for various reasons, but primarily because of the novelty of this paradigm, as well as the use of other advanced methods, which is briefly analyzed in this paper.
Research on Literature Aesthetic Education Based on Neural Network Algorithm

Abstract Chinese traditional Chinese language and literature learning mode emphasizes the application of knowledge, and pays attention to strengthening students’ memory of literary knowledge and literary theory in the process of Chinese language and literature learning, but the cultivation of aesthetic education has not been implemented. With the great development of ideology and culture, literary workers have created a large number of literary works, which poses new challenges to the application of natural language processing technology. Chinese language and literature, as a treasure of the Chinese nation, contains rich aesthetic elements and plays an important role in improving people’s humanistic cultivation and cultivating people’s aesthetic consciousness. In the teaching of Chinese language and literature, aesthetic education is beneficial to highlight the core connotation of the major and promote the overall development of students. On the basis of expounding the value connotation of aesthetic education in Chinese language and literature teaching, this paper analyzes the application of aesthetic education in Chinese language and literature teaching, and explores the specific implementation path of aesthetic education in Chinese language and culture specialty based on neural network algorithm.
Intelligent Prediction and Optimization Algorithm for Chronic Disease Rehabilitation in Sports Using Big Data

This paper investigates chronic diseases in the older population in the Chinese province of Henan and analyzes the rehabilitation needs and the current supply of related services in different levels of medical and elderly care institutions. We explore the fundamental causes for the diversified needs and insufficient supply of chronic disease patients in professional medical services and daily care. Using big data and deep learning (DL) in the sports domain, we propose a novel and intelligent prediction system for chronic diseases. Our model explores effective sinking methods of high-quality medical resources, training and guidance practices, assistance and guidance measures, and the ability to improve the grassroots services so that more chronically ill populations can stay in the community family as long as possible. In such an environment, they can receive cheap, safe, and suitable services. It can also lead to further improvement in constructing the government's regional medical rehabilitation care service system and can formulate long-term care relevant compensation policies.
The extraction of terminology list using ParaConc for creating a quadrilingual dictionary

The lack of terminology and language resources for the under-resourced South African languages poses a serious problem for effective communication in specialised fields such as law, education, health, agriculture, science, and technology. The ability to use all South African languages in all contemporary fields requires the existence of relevant terminology and resources. The article aims to semi-automatically identify and extract terminology for creating an English, Xitsonga, Siswati, and isiNdebele quadrilingual dictionary. Given parallel texts in the four different languages, we use ParaConc to identify and extract terminology in one language and the corresponding translations in the other languages. In this study, English is used as the source language, while Xitsonga, Siswati, and isiNdebele are the target languages. This process allowed us to identify specific lexical items in the source language (manually) and their translation equivalents in the target languages (automatically). The result was a collection of extracted terminology lists that can be used to compile a specialised quadrilingual dictionary for English and three of the under-resourced languages. We show the usefulness of ParaConc to semi-automatically extract quadrilingual terminology lists, which, by creating quadrilingual dictionaries, will contribute to the development and promotion of multilingualism in South Africa.
S-VoteNet: Deep Hough Voting with Spherical Proposal for 3D Object Detection

Current 3D object detection methods adopt an analogous box prediction structure with the 2D methods, which predict center and size of the object simultaneously in a box regression procedure, leading to the poor performance of 3D detector to a great extent. In this work, we propose S-VoteNet, which converts the prediction of 3D bounding box into two parts: center prediction and size prediction. By introducing a novel spherical proposal, S-VoteNet uses vote groups to predict the center and radius of object rather than all parameters of 3D bounding box. The prediction of radius is used to constrain the object size, and the radius-based spherical center loss is applied to measure the geometric distance between the proposal and ground-truth. To make better use of the geometric information provided by point cloud, S-VoteNet aggregates seeds by the votes indices to generate seed groups. The seed groups are then used for box size regression and orientation estimation. By decoupling the localization and size estimation, our method effectively reduces the regression burden of the 3D detector. Experimental results on SUN RGB-D 3D detection benchmark demonstrate that our S-VoteNet achieves state-of-the-art performance by using only point cloud as input.
Lengthening the Period of a Linear Feedback Shift Register

A linear feedback shift register (LFSR) is the basic element of the pseudo-random generators used to generate a sequence of pseudo-random values for a stream cipher. It consists of several cells; each cell is a flip-flop and a feedback function. The feedback function is a linear polynomial function; this function has a degree equal to the number of cells in the register. The basic elements of the register are connected to each other in two different ways, either in Fibonacci mode or in Galois mode. In the best case, the length of an LFSR is equal to two to the power of the number of cells of this register minus one, which is very low for cryptographic applications. To increase this length, one must look for primitive polynomials of great degree or to use adequate methods to lengthen LFSR with a reduced number of cells and, this is the objective of this work. Our method of lengthening of period of a LFSR is based on the logistics map.
A Framework for Reviewer Recommendation Based on Knowledge Graph and Rules Matching

With the increasing number of academic researchers, a wide range of papers are produced. As such, reviewer assignment stands out in ensuring the high quality of academic research. However, manual reviewer assignment faces a great difficulty due to the vast diversity of research topics. The complexity of these research topics also adds to the difficulty in reviewer-paper match. To solve these problems, we design a reviewer assignment system based on knowledge graph and rules matching. This design first extracts the keywords of the papers with a hierarchical semantic representation. Then, the rule engine is established by means of the knowledge graph and rules matching in order to reach a fast recommendation process. This design improves the efficiency and the correctness of the reviewer assignment process and provides sufficient interpretability for the recommended results.
Efficient Integer-Arithmetic-Only Convolutional Networks with Bounded ReLU

To facilitate large-scale deployment of convolutional networks, integer-arithmetic-only inference has been demonstrated effective, which not only reduces computational cost but also ensures cross-platform consistency. However, previous studies on integer networks usually report a decline in the inference accuracy, given the same number of parameters as floating-point-number (FPN) networks. In this paper, we propose to finetune and quantize a well-trained FPN convolutional network to obtain an integer convolutional network. Our key idea is to adjust the upper bound of a bounded rectified linear unit (ReLU), which replaces the normal ReLU and effectively controls the dynamic range of activations. Based on the tradeoff between learning ability and quantization error of networks, we managed to preserve full accuracy after quantization and obtain efficient integer networks. Our experiments on ResNet for image classification demonstrate that our 8-bit integer networks achieve state-of-the-art performance compared with Google's TensorFlow and NVIDIA's TensorRT. Moreover, we experiment on VDSR for image super-resolution and on VRCNN for compression artifact reduction, both of which serve regression tasks that natively require high inference accuracy. Besides ensuring the equivalent performance as the corresponding FPN networks, our integer networks have only 1/4 memory cost and run 2× faster on GPUs.
Gradient-Based Adversarial Attacks on Categorical Sequence Models via Traversing an Embedded World

Deep learning models suffer from a phenomenon called adversarial attacks: we can apply minor changes to the model input to fool a classifier for a particular example. The literature mostly considers adversarial attacks on models with images and other structured inputs. However, the adversarial attacks for categorical sequences can also be harmful. Successful attacks for inputs in the form of categorical sequences should address the following challenges: (1) non-differentiability of the target function, (2) constraints on transformations of initial sequences, and (3) diversity of possible problems. We handle these challenges using two black-box adversarial attacks. The first approach adopts a Monte-Carlo method and allows usage in any scenario, the second approach uses a continuous relaxation of models and target metrics, and thus allows a usage of state-of-the-art methods for adversarial attacks with little additional effort. Results for money transactions, medical fraud, and NLP datasets suggest that the proposed methods generate reasonable adversarial sequences that are close to original ones, but fool machine learning models.
Study on Library Individualized Information Security Under the Background of Big Data

Under the background of big data, the issue of users' individual needs are increasingly prominent. This paper analyzes the potential information security problems of library users and finds that their individualized information is easy to be leaked, falsified or lost in the process of information collection and mining. To solve these problems, this paper has proposed an improved data encryption/decryption architecture to effectively improve the defense security of the system and shorten the data processing time as much as possible, and discussed the influence of different factors on the time consumption of information camouflage/information extraction to solve the data privacy security problem from the technical level.
Disruptive technologies in energy and environment

The global carbon emissions have increased over the years and as an effect has become a worldwide problem. The systematic use and management of the present technologies to improve energy performance and efficiency can have a huge impact on the environment and global carbon emissions. Few among the various emerging technologies are Artificial Intelligence, Automation, IoT, and Blockchain, blockchain still being in its infant stage of growth in energy sectors and industries. This paper presents a few of the available sources in each of these sectors, supported by simulations and existing practices being implemented.
Killing Four Birds with one Gaussian Process: The Relation between different Test-Time Attacks

In machine learning (ML) security, attacks like evasion, model stealing or membership inference are generally studied in individually. Previous work has also shown a relationship between some attacks and decision function curvature of the targeted model. Consequently, we study an ML model allowing direct control over the decision surface curvature: Gaussian Process Classifiers (GPCs). For evasion, we find that changing GPC's curvature to be robust against one attack algorithm boils down to enabling a different norm or attack algorithm to succeed. This is backed up by our formal analysis showing that static security guarantees are opposed to learning. Concerning intellectual property, we show formally that lazy learning does not necessarily leak all information when applied. In practice, often a seemingly secure curvature can be found. For example, we are able to secure GPC against empirical membership inference by proper configuration. In this configuration, however, the GPC's hyper-parameters are leaked, e.g. model reverse engineering succeeds. We conclude that attacks on classification should not be studied in isolation, but in relation to each other.
Verbal Abuse Classification Using Multiple Deep Neural Networks

People can be exposed to verbal abuse practically anywhere. It is considered to be one of serious issues in society. In this paper, we describe a method to classify verbal abuse into five lasses by adding a convolutional neural network (CNN), a long short-term memory, and a dense layer on top of bidirectional encoder representations from transformers (BERT). The data are collected from Korean drama, movies, and YouTube. Due to data imbalance, weighted random sampler and data augmentation are used to train the models to be generalized. Experiments show that BERT with CNN after data augmentation performs the highest accuracy among all the compared methods.
Expanding Semantic Knowledge for Zero-Shot Graph Embedding

Zero-shot graph embedding is a major challenge for supervised graph learning. Although a recent method RECT has shown promising performance, its working mechanisms are not clear and still needs lots of training data. In this paper, we give deep insights into RECT, and address its fundamental limits. We show that its core part is a GNN prototypical model in which a class prototype is described by its mean feature vector. As such, RECT maps nodes from the raw-input feature space into an intermediate-level semantic space that connects the raw-input features to both seen and unseen classes. This mechanism makes RECT work well on both seen and unseen classes, which however also reduces the discrimination. To realize its full potentials, we propose two label expansion strategies. Specifically, besides expanding the labeled node set of seen classes, we can also expand that of unseen classes. Experiments on real-world datasets validate the superiority of our methods.
Research on Unconstrained Face Recognition Based on Deep Learning

The emergence of deep learning has greatly promoted the development of the field of face recognition. The accuracy of face recognition in real scenes is affected by many factors. Among them, the problem of multiple poses is still an external factor that is difficult to overcome in face recognition. For the identification process in the Central African people face extreme attitude with the state led to the problem of low recognition accuracy, this paper proposes a gesture of deep space based correction feature to improve the recognition accuracy of multi-pose, first proposed in 2019 to use Google's lightweight network MobileNet for attitude correction in deep space, additionally employed ResNet18 verify and compare recognition results. This paper uses the VggFace2 dataset to train the two models in an end-to-end manner, and then test them on the CFP dataset, IJB-A dataset, and LFW dataset. The results show that the two backbone models proposed in the article are not much different from ResNet50 on the CFP. The face recognition on the IJB-A dataset is around 96%. The average recognition on the public data set LFW is about 96%. From the results of the test set, the model in the article is better than other methods. In addition, MobileNetV3 has a better recognition accuracy than ResNet18, and the amount of calculation is smaller.
Multi-Tenant Intrusion Detection Framework as a Service for SaaS

Information technology (IT) service providers are nowadays moving toward cloud computing. Software-as-a-service (SaaS) refers to cloud service-oriented web applications. As a result of computation outsourcing, a customer (tenant) can subscribe to a self-service SaaS and use it on a pay-per-use basis. To reduce resource costs, a single instance of SaaS serves multiple tenants (multi-tenancy). However, outsourcing and multi-tenancy bring about new security issues. Indeed, tenants lose control over the source code, databases and infrastructure and cannot deploy their own intrusion detection system (IDS). In this context, the provider must not only integrate their preferred IDS into a public cloud, but also protect the tenants according to their individual security requirements. We put forth a multi-tenant intrusion detection framework as a service for SaaS (MTIDaaS) to allow the provider to undertake such integration. Our MTIDaaS has been integrated and tested in a real public cloud environment. It provides security-as-a-service (SecaaS) for both provider and tenant with high levels of portability, flexibility and cost-effectiveness. The experimental results demonstrate that our MTIDaaS offers easy integration of IDS with little virtualization overhead and insignificant impact on HTTP response time.
[Detection of microcalcification clusters regions in mammograms combining discriminative deep belief networks].

In order to overcome the shortcomings of high false positive rate and poor generalization in the detection of microcalcification clusters regions, this paper proposes a method combining discriminative deep belief networks (DDBNs) to automatically and quickly locate the regions of microcalcification clusters in mammograms. Firstly, the breast region was extracted and enhanced, and the enhanced breast region was segmented to overlapped sub-blocks. Then the sub-block was subjected to wavelet filtering. After that, DDBNs model for breast sub-block feature extraction and classification was constructed, and the pre-trained DDBNs was converted to deep neural networks (DNN) using a softmax classifier, and the network is fine-tuned by back propagation. Finally, the undetected mammogram was inputted to complete the location of suspicious lesions. By experimentally verifying 105 mammograms with microcalcifications from the Digital Database for Screening Mammography (DDSM), the method obtained a true positive rate of 99.45% and a false positive rate of 1.89%, and it only took about 16 s to detect a 2 888 × 4 680 image. The experimental results showed that the algorithm of this paper effectively reduced the false positive rate while ensuring a high positive rate. The detection of calcification clusters was highly consistent with expert marks, which provides a new research idea for the automatic detection of microcalcification clusters area in mammograms.为了克服乳腺图像微钙化簇检测中假阳性率高、泛化性差等缺点，本文提出了一种结合判别式深度置信网络（DDBNs）自动快速定位乳腺 X 线图像中微钙化簇区域的方法。首先，对乳腺区域进行提取及增强，将增强后的乳腺区域进行子块重叠分割和小波滤波；之后，构建用于乳腺子块特征提取和分类的 DDBNs 模型，将预训练后的 DDBNs 转换成使用 softmax 分类器的深度神经网络（DNN），并通过反向传播对网络进行微调；最后，输入待检乳腺 X 线图像，完成可疑病灶区域的定位。通过对乳腺摄影筛查数据库（DDSM）中的 105 幅含有微钙化点的图像进行实验验证，本文方法获得了 99.45% 的真阳性率和 1.89%的假阳性率，且检测一幅 2 888 × 4 680 大小图像的时间约 16 s。实验结果表明：该算法在保证较高真阳性率的同时有效地降低了假阳性率，检测到的微钙化簇区域与专家标记区域具有高度一致性，为乳腺 X 线图像中微钙化簇区域的自动检测提供了新的研究思路。.
An information management system for effective disaster relief operations

Abstract During an emergency situation, there is often confusion in information that can complicate disaster management efforts. For this reason, it is necessary to design an information system that can support emergency response operations. The purpose of designing this information system is to design a disaster management information system to strengthen emergency response operations quickly, precisely, and dynamically, and can be a decision supporter to facilitate emergency response operations. The system was developed using the waterfall method using the Object-Oriented Programming (OOP) approach. The design of information systems uses the PHP programming language and uses MySQL as a database. Then the system that has been designed is verified and validated using the Black Box method. The results of the information system design can provide dynamically needed information so that it can assist in scheduling and allocating volunteers, verifying data from donors, making TRC reports, collecting data and refugee needs, and estimating the distance between posts and evacuation sites
Generating Constrained Multi-target Scene Images Using Conditional SinGAN

As a variant of generative adversial network(GAN), SinGAN has been attracting a lot of research interest. Aiming at the disadvantage that SinGAN can easily generate multi-target images that do not conform to human logic, and the randomness of generating images is exsiting, simutaneously, the controllability of the generated images are poor, we have improved SinGAN's architecture and loss function, and made it a conditional GAN instead of the original nonconditional GAN, so as to make the images more reasonable in spatial layout and semantic information. Conditional SinGAN realizes the function of manipulating the number and layout of the target in the image with the subjective will of the users. The experimental results show that conditional SinGAN has achieved ideal effect.