Reviewerly/exHarmony huggingface dataset:
Generalized Inverse Based Decoding

The concept of Generalized Inverse Decoding (GID) is introduced, as an algebraic framework for the syndrome decoding (SD) and low-weight codeword (LWC) problems. The framework has ground on two characterizations by generalized inverses, one for the null space of a matrix and the other for the solution space of a system of linear equations over a finite field. Generic GID solvers are proposed for the SD and LWC problems. It is shown that information set decoding (ISD) algorithms, such as Prange, Lee-Brickell, Leon, and Stern’s algorithms, are particular cases of GID solvers. All of them search generalized inverses or elements of the null space under various specific strategies. However, as the paper shows in the case of Prange’s algorithm, they do not search through the entire space, while our solvers do even when they use just one Gaussian elimination. Apart from these, our GID framework clearly shows how each ISD algorithm except for Prange’s can be used as an SD or LWC solver. Experimental results show a very good behavior of the GID solvers. The domain of easy weights can be reached by a very few iterations and even enlarged.
Animals Image Classification Method Based on Improved Convolutional Neural Network

This paper attempts to apply neural networks to classify images of 10 different animals. The dataset of the classification model is derived from tens of thousands of animal maps on the network, and these images have been artificially checked in advance. Convolutional neural network is a very popular way of image classification, is an important part of iconography. This article focuses on the analysis of the influence of the parameters of the algorithm we use on the model in order to optimize the model. Through the analysis of the network structure, we have improved the model using different methods to achieve a better model structure and application to classification. The final training accuracy is over 85% and the text precision is approximately 75%.
Binary Classification Under ℓ<sub>0</sub> Attacks for General Noise Distribution

Adversarial examples have recently drawn considerable attention in the field of machine learning due to the fact that small perturbations in the data can result in major performance degradation. This phenomenon is usually modeled by a malicious adversary that can apply perturbations to the data in a constrained fashion, such as being bounded in a certain norm. In this paper, we study this problem when the adversary is constrained by the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> norm; i.e., it can perturb a certain number of coordinates in the input, but has no limit on how much it can perturb those coordinates. Due to the combinatorial nature of this setting, we need to go beyond the standard techniques in robust machine learning to address this problem. We consider a binary classification scenario where d noisy data samples of the true label are provided to us after adversarial perturbations. We introduce a classification method which employs a nonlinear component called truncation, and show in an asymptotic scenario, as long as the adversary is restricted to perturb no more than $\sqrt d $ data samples, we can almost achieve the optimal classification error in the absence of the adversary, i.e. we can completely neutralize adversary’s effect. Surprisingly, we observe a phase transition in the sense that using a converse argument, we show that if the adversary can perturb more than $\sqrt d $ coordinates, no classifier can do better than a random guess.
Adaptive Importance Sampling Message Passing

The aim of Probabilistic Programming (PP) is to automate inference in probabilistic models. One efficient realization of PP-based inference concerns variational message passing-based (VMP) inference in a factor graph. VMP is efficient but in principle only leads to closed-form update rules in case the model consists of conjugate and/or conditionally conjugate factor pairs. Recently, Extended Variational Message Passing (EVMP) has been proposed to broaden the applicability of VMP by importance sampling-based particle methods for non-linear and non-conjugate factor pairs. EVMP automates the importance sampling procedure by employing forward messages as proposal distributions, which unfortunately may lead to inaccurate estimation results and numerical instabilities in case the forward message is not a good representative of the unknown correct posterior. This paper addresses this issue by integrating an adaptive importance sampling procedure with message passing-based inference. The resulting method is a hyperparameter-free approximate inference engine that combines recent advances in stochastic adaptive importance sampling and optimization methods. We provide an implementation for the proposed method in the Julia package ForneyLab.jl.
A cloud platform architecture recovery metric method

Cloud platforms have become the most popular architecture for providing software services today, and traditional architecture evolution evaluation methods cannot achieve better results on cloud platforms because they are different from the architecture of traditional software. In this paper, we propose a method for architecture recovery based on the decentralized characteristics of cloud platforms. First, the method identifies the inapplicability of traditional monolithic software architecture recovery methods under the cloud platform architecture, and then a fast recovery method for cloud platform architecture based on the new city of dispersion features. The process of cloud platform architecture recovery extracts the overall architecture information, integrates the transformed New Town association dependency graph based on the architecture information, and then uses the architecture dependency graph as the input for distance-based aggregation of decentralized components. Finally, the above method is applied to three cloud platform projects, and the evaluation of the effectiveness of architecture recovery is verified separately. The experimental results show that the cloud platform architecture recovery method based on decentralized features can fully extract the cloud platform architecture information and achieve the accurate metric of architecture description.
A Social Recommendation Model based on dual attention mechanism

Social recommendation can effectively alleviate the information overload problem faced by users in social platforms. However, in the existing recommendation methods, problems such as sparse data and low recommendation accuracy do exist. Previous studies have paid more attention to explicit feedback data such as review ratings, and often ignored the influence of implicit feedback data such as social trust relationship on user interest preferences. In response to these problems, this paper first proposes Social Relationship Recommendation Model with Multi-level attention (MA_SRec), to solve the problem that deep and shallow social trust relationships have variant degree of impacts on users' interest preferences. The model uses LSTM Long Short-Term Memory network to extract user dynamic trust network, learns the influence weight of recent social trust relationship through attention mechanism. Assigning respective influence to features can accurately describe the user's interest preferences. This paper draws on the real information and social relationship data set Epinions, and compares MA_SRec with the social algorithm SErec, SBPR, implicit feedback recommendation algorithm WRMF and other classic algorithms. The four indicators of MA_SRec have improved significantly. The experimental results show that the method can deeply analyze social information and effectively improve the problem of data sparsity. This model supports application in information recommendation and various social recommendation scenarios.
Lasso Regression Based on Halton Sequence Initialized Capuchin Search Algorithm

Least absolute shrinkage and selection operator (Lasso) regression is an important regression method of supervised learning in the field of artificial intelligence (AI). Its essence is to add L1 regularization term to multiple linear regression, so as to improve the sparsity of function solution. However, the objective function of Lasso regression optimization is not differentiable, thus, in previous studies, sub gradient descent method and the coordinate descent method have been used to solve it, but those methods are easy to fall into the trap of local minimum and cannot find the global optimal solution. The drawback of those methods affects the operation efficiency of Lasso regression algorithm. In order to overcome the drawback, in this paper, a new Halton sequence initialized capuchin search algorithm (HSCSA) is proposed to replace those two methods to quickly and accurately obtain the global optimal solution of the objective function of Lasso regression. The experimental results show that HSCSA is better than the previous two methods in operation speed and operation efficiency.
Improving A Domain-Specific Sentence Semantic Representation with Non-Relevant Domain Data

The semantic vector representation of sentences in specific domain has important uses and plays an important role in question answering and retrieval. However, the semantic representation of sentences obtained based on neural networks requires sufficient training data. And a specific domain may lack data for semantic training, and it is difficult to obtain such data. And there may already be a lot of data for training languages in many other specific domains. This paper attempts to use these semantic training data independent of the target domain to train a semantic model for the target domain to help improve the semantic representation of this domain. Experiments show that using a BERT-based semantic representation model, trained with non-relevant domain data, can effectively improve the semantic vector representation of target specific domain.
A Fine-grained Certificateless Conditional Proxy Broadcast Re-Encryption Scheme Without Pairing

The certificateless proxy re-encryption eliminates the need for public key certificate in traditional public-key cryptography and key-escrow problem in identity-based cryptography, and allows a semi-trusted proxy to convert the cipher-text of the data owner into the ciphertext of the target user, which makes it more convenient to apply. However, the existing certificateless proxy re-encryption schemes cannot support one-to-many conversion and flexible control on convert conditions. In this paper, we propose a fine-grained certificateless conditional proxy broadcast re-encryption scheme without pairing. In our scheme, the data owner can delegate the decryption rights of the ciphertext that meets certain conditions to the target users, and the data owner can revoke the delegate rights. At the same time, no pairing is used, which makes the scheme more efficient. Finally, we show that the security of the scheme for chosen-ciphertext attacks in the random oracle model.
A semi-supervised image segmentation method based on generative adversarial network

Semantic segmentation based on deep learning is an important research direction in computer vision, and it has developed rapidly in intelligent interpretation of remote sensing images in recent years. However, semantic segmentation based on deep learning is also a long-standing challenge in remote sensing image applications. The main reason is that training a neural network requires a large amount of pixel-level labeled data, and these publicly available labeled data are usually few. It requires a lot of labor costs. To address this issue, this paper proposes a semi-supervised semantic segmentation method based on Generative adversarial networks (GAN). Compared with previous techniques, we extends typical GAN networks to pixel-level prediction and discrimination, and applies it to remote sensing image semantic segmentation. We introduce residual networks and dilated convolutions into the generator, and employ a flow alignment module (FAM) to learn the semantic flow between adjacent hierarchical feature maps. The connected discriminator formulates the training as the min-maximum optimization problem. Comprehensive quantitative and qualitative evaluations on multiple models show that our proposed method outperforms state-of-the-art semi-supervised image segmentation algorithms.
An improved Tiny-YOLOv3 and its implementation with FPGA

Tiny-YOLOv3 is a simplified YOLO algorithm and has the characteristics of simple network model and small computational cost, which is very suitable for real-time target detection applications. Aiming at low accuracy of Tiny-YOLOv3 used in detecting small target objects, Tiny-YOLOv3 algorithm is improved by changing two-scale detection to three-scale detection and calculating the parallelism ratio of the loss function with CIoU (Complete-IoU) in this paper. Meanwhile, the BN (Batch Normalization) layer is merged into the convolutional layer, which speeds up the forward inference of Tiny-YOLOv3. The improved Tiny-YOLOv3 is realized in an FPGA (Field programmable gate array) to detect targets. The experimental results show that detection accuracy of the improved Tiny-YOLOv3 is increased by 48.6% and the detection speed of the improved Tiny-YOLOv3 is decreased by only 5% compared with Tiny- YOLOv3. It is suitable for realization on FPGA.
Improved ant colony algorithm based on multi-objective optimization control

Aiming how to achieve a high-precision furnace temperature curve in the production process of electronic products, a double-objective optimization model is established, which combines heat conduction and heat convection, and transforms it into a single objective to solve the optimal furnace temperature curve and important parameters through dynamic comprehensive weighting. In this paper, an improved ant colony algorithm has been proposed to address the issue. The population initialization method based on good points is set to make the initial population distribution more uniform. The nonlinear control parameter is utilized to balance the global search and local development ability during different iteration periods. The Markov process is executed to get a wider range of solutions. Compared with other intelligent algorithms, the results show that the improved ant colony algorithm avoids local premature phenomenon, has higher convergence, and the obtained results are more accurate. Using this algorithm to solve the problem can better face the field of precise temperature control.
A GAN-based Method for Generating SQL Injection Attack Samples

Due to the simplicity of implementation and high threat level, SQL injection attacks are one of the oldest, most prevalent, and most destructive types of security attacks on Web-based information systems. With the continuous development and maturity of artificial intelligence technology, it has been a general trend to use AI technology to detect SQL injection. The selection of the sample set is the deciding factor of whether AI algorithms can achieve good results, but dataset with tagged specific category labels are difficult to obtain. This paper focuses on data augmentation to learn similar feature representations from the original data to improve the accuracy of classification models. In this paper, deep convolutional generative adversarial networks combined with genetic algorithms are applied to the field of Web vulnerability attacks, aiming to solve the problem of insufficient number of SQL injection samples. This method is also expected to be applied to sample generation for other types of vulnerability attacks.
Bidirectional Deep Variational RNN for Refining Anomaly Detection of Multivariate Time Series

To characterize the complex temporal dependence and stochasticity of multivariate time series (MTS) and achieve their unsupervised anomaly detection, which is an essential task in service quality management of industry devices or Internet infrastructures, in this paper, we propose a bidirectional deep variational RNN (bi-DVRNN), a hierarchical probabilistic dynamical model incorporating bidirectional structures, that can achieve refined anomaly detection at a certain timestep with both past and future information of a specific time frame. Specifically, the core idea of bi-DVRNN is capture the normal patterns of input MTS by collecting transmitted information and long-range temporal dependencies from both directions through deep recurrent structures and hierarchical probabilistic generative operations, and then apply reconstruction probabilities to determine anomalies. For efficient inference of the proposed models, we construct a novel deterministic-upward-stochastic-downward autoencoding inference scheme to achieve an accurate posterior approximation of latent variables. The evaluation experiments conducted on two public datasets show the efficiency of bi-DVRNN in refining anomaly detection results.
Research and Application of Gaze Data Processing Method

Gaze is a way of finding and transmitting information, which can directly or indirectly reflect people's true intentions. Current gaze research usually focuses on the improvement of gaze estimation techniques, and rarely studies gaze from the perspective of data processing. Due to the low value density of gaze data, it will be difficult to capture effective gaze without data processing. Therefore, we propose a gaze data processing method based on artificial neural network and face recognition technology. The effectiveness of the method is verified by experiments. Finally, we combine gaze data processing method and gaze estimation technology to provide an application case of gaze correlation analysis for this method.
Attention-based Generative Adversarial Hashing for Cross-modal Retrieval

This paper proposes an adversarial hash cross-modal retrieval method based on a multimodal interactive attention mechanism. By constructing a cross-modal shared subspace representation learning network KGRU, the global semantic and local semantic information of each modality is extracted, and then introduce the attention mechanism into the model to further capture the key local feature information in different modalities. The experimental results show that this method can accurately mine the structural information and semantic correlation between modalities, and improve the accuracy of cross-modal retrieval. Improve computing time efficiency. The state-of-the-art effect is obtained in cross-modal retrieval experiments on the NUS-WIDE dataset.
Differentiable Filter Pruning with Attention For Surface Object Detection Network

The memory requirements and large computing costs make it difficult to deploy object detection networks on unmanned surface vessel embedded platform. Many existing pruning approaches are often based on heuristic criteria, and require manual setting of the compression ratios in each layer. It makes network pruning more difficult and inaccurate. In this paper, we propose a differentiable filter pruning framework with attention(DPA) for the surface object detection network. We added light channel attention module to estimate filter's importance on the original network. After the attention module, we designed a differentiable sparse mask layer, which can mask the unimportant filters without manually setting the compression ratios. Our proposed approach pruned the YOLO-v4 network and the mAP of the pruned model was 0.6% higher than the original model, with a reduction of 70% parameters and 50% FLOPs. In addition, our approach also achieved excellent results on CIFAR-10/100.
Comparison of Hands-Free Speech-Based Navigation Techniques for Virtual Reality Training

When it comes to Virtual Reality (VR) training, the depicted scenarios can be characterized by a high level of complexity and extent. Speech-based interaction techniques can provide an intuitive, natural and effective way to navigate large Virtual Environments (VEs) without the need for handheld controllers, which may impair the execution of manual tasks or prevent the use of wearable haptic devices. In this study, three hands-free speech-based navigation techniques for VR, a speech-only technique, a speech with gaze variant (gaze to point to the destination, speech as trigger), and a combination of the fist two are compared by deploying them to a large VE representing a common industrial setting (a hangar). A within-subjects user study was carried out in order to assess the usability and the performance of the considered techniques.
A new framework to classify the cancerous and non-cancerous pap smear images using filtering techniques to improve accuracy

Cervical cancer is not new to the research at the same time it has more impact on society to motivate to find better solutions to predict at the earlier stage to avoid the severity of the patient. Cervical cancer has various stages of severity and it will be analyzed using various diagnosis methods. Therefore to identify the severity, this research study will use machine learning approaches to analyze the medical diagnosis inputs. In this study, the cancerous pap smear images are given as input on machine learning algorithms and predicted the severity of decease for better treatment. Therefore this research paper proposes a new framework to classify the cancerous and non-cancerous pap smear images using filtering techniques to improve the better accuracy of the existing research studies.
Virtual Machine allocation in multiple Data Centers using Throttled Load Balancing to improve the performance in Cloud

Today, Cloud Computing is a distributed system environment. These days the services are available pay as you go model. Cloud users are paying as per their services in the cloud environment. The services available to the Cloud users are Infrastructure as a service, platform as a service, software as a service and security as a service. Nowadays, most users are migrating to cloud platforms. In Covid - 19 pandemic situation, most large and small scale organizations operating their business using cloud platforms. On the other end due to industrial automation, the companies switched their operations to a cloud environments. Due to the rapid business migration, the demand for cloud computing increased. With the increase of demand in the cloud, the service providers are satisfied. On the other end, a challenging issue is resource allocation. The best resource allocation strategy will provide quick services to the cloud users and minimum cost to the cloud providers. In this paper, we will discuss, resource allocation procedure, the throttled load balancing algorithm and the results are compared with other resource optimization techniques.
Feature Based Opinion Mining for Mobile Reviews

Item surveys or client criticism has become an ideal platform for sellers to promote their goods and clients to expand their knowledge and purchase wisely. As the online system of buying and selling is expanding, a measure of client audits additionally has been expanded undeniably. Thus it is an intense assignment for retailers just as clients to peruse the surveys related with the item. Notion examination settle this issue by looking over free content audits and giving the assessment synopsis. Highlight based supposition based investigation strategies expands the granularity of notion of examination by dissecting extremity related with highlights in the given free content. The main objective of this work is to plan a system that predicts extremity and to plan a score which determines the stratum of extremity. Resulting component-level scores are summed up as per the clients&#x2019; need of interest.