Reviewerly/exHarmony huggingface dataset:
Adversarial Training Time Attack Against Discriminative and Generative Convolutional Models

In this paper, we show that adversarial training time attacks by a few pixel modifications can cause undesirable overfitting in neural networks for both discriminative and generative models.We propose an evolutionary algorithm to search for an optimal pixel attack using a novel cost function inspired by domain adaptation literature to design our training time attack.The proposed cost function explicitly maximizes the generalization gap and domain divergence between clean and corrupted images.Empirical evaluations demonstrate that our adversarial training attack can achieve significantly low testing accuracy (with high training accuracy) on multiple datasets by just perturbing a single pixel in the training images.Even under the use of popular regularization techniques, we identify a significant performance drop compared to clean data training.Our attack is more successful than previous pixel-based training time attacks on state-ofthe-art Convolutional Neural Networks (CNNs) architectures, as evidenced by significantly lower testing accuracy.Interestingly, we find that the choice of optimization plays an essential role in robustness against our attack.We empirically observe that Stochastic Gradient Descent (SGD) is resilient to the proposed adversarial training attack, different from adaptive optimization techniques such as the popular Adam optimizer.We identify that such vulnerabilities are caused due to over-reliance on the cross-entropy (CE) loss on highly predictive features.Therefore, we propose a robust loss function that maximizes the mutual information between latent features and input images, in addition to optimizing the CE loss.Finally, we show that the discriminator in Generative Adversarial Networks (GANs) can also be attacked by our proposed training time attack resulting in poor generative performance.Our paper is one of the first works to design attacks for generative models.
Proposing a Load Balancing Algorithm For Cloud Computing Applications

Abstract At the current time, one of the fields that are quickly developing in computing is clouding computing (CC). There are vital services needed by individuals as well as organizations that are offered in CC, namely IaaS, SaaS, and PaaS. Through the creation of the cloud, the development of the application has become easier, as well as providing services to end-users through virtualization over the internet. In CC, we have cloud service providers who deal with large computing structures completely defined on usage as they offer their services in a dependable mode. However; CC is facing the issue of load balancing, which impacts the performance and makes it weak if we overload the system. Developing an effective load balancing algorithm while utilizing CC efficiently is one of the providers’ ultimate goal. Virtualization and scalability are other dynamic techniques which entail developing VMs in CC. Data traffic and Web services provision is growing each day; thus, load balancing is a major challenge in the field of CC and is increasing the importance of task scheduling as well. At this point in time, evolvement in CC necessitates the greater need for infrastructure and resource development. The load balancing policy guarantees the efficient usage of the resource through the provision of services to the cloud subscribers. Load balancing also prioritizes subscribers by using an appropriate schedule. As a contribution in research, this paper proposes an algorithm for load balancing in CC. Proposed algorithm enhance the performance of Cloud Computing applications
A Survey of Nearest-Better Clustering in Swarm and Evolutionary Computation

Nearest-Better Clustering (NBC) is an emergent niching technique in Swarm and Evolutionary Computation for optimization, which does not need to fix the number or radius of clusters in advance. The key idea of NBC is to first link each individual to its nearest better neighbor to form a spanning tree of all individuals in the population, and then partition all individuals into clusters by deleting the longer edges in the spanning tree. In this paper, a survey on the Nearest-Better Clustering algorithms and applications in multimodal and dynamic optimization is provided. First, the basic NBC algorithm is introduced. Second, the improvements of the basic NBC are detailed. Third, multimodal and dynamic optimization algorithms powered by NBC are enlisted and discussed.
YOG-Assist

Performing yoga requires training from the experts as it contains asanas which needs to be performed correctly for its best results. If a person doesn't have time for taking special training from the expert, then the person should be self-trained for understanding the correct pose. In this paper a model is proposed which can differentiate the right and wrong postures. The paper focuses on classifying the postures of Surya Namaskar asanas. Initially, by performing literature review we decided to use the OpenPose technique which is an open source pretrained model based on Caffe framework developed by CMV-Perceptual-Computing-Lab. Then dataset was created by capturing images of the people performing Surya Namaskar from Yoga Academy. The pre-trained model of OpenPose was used to extract the skeletons of the poses in images. Then a CNN model was built and trained on the above dataset. The validation and testing accuracy, and confusion matrix were found.
Distance transform based text-line extraction from unconstrained handwritten document images

Text-line extraction (TLE) is the process of segmenting a document page into lines of text for processing by modules such as language and writer identification or Optical Character Recognition (OCR). Designing of an appropriate TLE method is always a challenging research problem especially in the domain of unconstrained handwritten documents. This is because of the vast number of potential interactions between the text lines. For example, these lines are not always straight, lines written close to each other can overlap with ascenders and descenders, and lines can interact with other content on the page. In this paper, we present a novel language-independent text-line extraction method for unconstrained handwritten documents which handles complexities such as touching and multi-skewed text lines, overlapping characters and irregular inter-line spacing. Our method preprocesses the document pages using a distance transform based method and uses a novel path detection algorithm to separate individual text-line. The proposed method has been tested on six standard datasets which are publicly available and the experimental results show that our method achieves a promising accuracy over state-of-the-art TLE methods.
Quadratic Secret Sharing and Conditional Disclosure of Secrets

There is a huge gap between the upper and lower bounds on the share size of secret-sharing schemes for arbitrary n-party access structures, and consistent with our current knowledge the optimal share size can be anywhere between polynomial in n and exponential in n. For linear secret-sharing schemes, we know that the share size for almost all n-party access structures must be exponential in n. Furthermore, most constructions of efficient secret-sharing schemes are linear. We would like to study larger classes of secret-sharing schemes with two goals. On one hand, we want to prove lower bounds for larger classes of secret-sharing schemes, possibly shedding some light on the share size of general secret-sharing schemes. On the other hand, we want to construct efficient secret-sharing schemes for access structures that do not have efficient linear secret-sharing schemes. Given this motivation, Paskin-Cherniavsky and Radune (ITC'20) defined and studied a new class of secret-sharing schemes in which the shares are generated by applying degree-d polynomials to the secret and some random field elements. The special case $$d=1$$ corresponds to linear and multi-linear secret-sharing schemes. We define and study two additional classes of polynomial secret-sharing schemes: (1) schemes in which for every authorized set the reconstruction of the secret is done using polynomials and (2) schemes in which both sharing and reconstruction are done by polynomials. For linear secret-sharing schemes, schemes with linear sharing and schemes with linear reconstruction are equivalent. We give evidence that for polynomial secret-sharing schemes, schemes with polynomial sharing are probably stronger than schemes with polynomial reconstruction. We also prove lower bounds on the share size for schemes with polynomial reconstruction. On the positive side, we provide constructions of secret-sharing schemes and conditional disclosure of secrets (CDS) protocols with quadratic sharing and reconstruction. We extend a construction of Liu et al. (CRYPTO'17) and construct optimal quadratic k-server CDS protocols for functions with message size $$O(N^{(k-1)/3})$$ . We show how to transform our quadratic k-server CDS protocol to a robust CDS protocol, and use the robust CDS protocol to construct quadratic secret-sharing schemes for arbitrary access structures with share size $$O(2^{0.705n})$$ ; this is better than the best known share size of $$O(2^{0.7576n})$$ for linear secret-sharing schemes and worse than the best known share size of $$O(2^{0.585n})$$ for general secret-sharing schemes.
Straggler Mitigation through Unequal Error Protection for Distributed Matrix Multiplication

Large-scale machine learning and data mining methods routinely distribute computations across multiple agents to parallelize processing. The time required for computation at the agents is affected by the availability of local resources giving rise to the "straggler problem" in which the computation results are held back by unresponsive agents. For this problem, linear coding of the matrix sub-blocks can be used to introduce resilience toward straggling. The Parameter Server (PS) utilizes a channel code and distributes the matrices to the workers for multiplication. It then produces an approximation to the desired matrix multiplication using the results of the computations received at a given deadline. In this paper, we propose to employ Unequal Error Protection (UEP) codes to alleviate the straggler problem. The resiliency level of each sub-block is chosen according to its norm as blocks with larger norms have higher effects on the result of the matrix multiplication. We validate the effectiveness of our scheme both theoretically and through numerical evaluations. We derive a theoretical characterization of the performance of UEP using random linear codes, and compare it the case of equal error protection. We also apply the proposed coding strategy to the computation of the back-propagation step in the training of a Deep Neural Network (DNN), for which we investigate the fundamental trade-off between precision and the time required for the computations.
Research on Consumer Purchasing Prediction Based on XGBoost Algorithm

To predict how many consumers will buy goods in the next month helps the e-commerce platform discover potential buyers and carry out the corresponding strategic activities. After analyzing and cleaning the data, we select user purchase features to use eXtreme Gradient Boosting (XGBoost) algorithm to train the divided data sets. Meanwhile, we choose Light Gradient Boosting Machine (LightGBM), Long Short-Term Memory (LSTM) and Fully Connected Neural Network (FCNN) as comparison algorithms. Expectedly, the experiments indicate that using the XGBoost algorithm to predict purchasing can improve performance. Specifically, LightGBM and LSTM increase significantly before remaining stable, whereas FCNN begins in the highest number falling dramatically to approximately the accuracy of 0.32 and keeps steady. Throughout the iteration process, the accuracy of XGBoost surpassed FCNN, and experienced a moderate increase from 0.55 to 0.67, increasing the accuracy by 12%.
Static Palm Sign Gesture Recognition with Leap Motion and Genetic Algorithm

Sign gesture recognition is the field that models sign gestures in order to facilitate communication with hearing and speech impaired people. Sign gestures are recorded with devices like a video camera or a depth camera. Palm gestures are also recorded with the Leap motion sensor. In this paper, we address palm sign gesture recognition using the Leap motion sensor. We extract geometric features from Leap motion recordings. Next, we encode the Genetic Algorithm (GA) for feature selection. Genetically selected features are fed to different classifiers for gesture recognition. Here we have used Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) classifiers to have their comparative results. The gesture recognition accuracy of 74.00% is recorded with RF classifier on the Leap motion sign gesture dataset.
A UAV development platform for intelligent applications

To date, many UAV systems have been developed worldwide to operate at locations where access proved difficult, such as disaster zones, with the final aim being object transportation, inspection, identification, and mapping among others. In this respect, in recent years various researches have been done to develop aerial robots that have led, in turn, to many different types of UAV systems with varied kinds of structures, capabilities, and final scopes. In this research, we propose a UAV development platform for intelligent applications. Exactly, it enables fast real-time implementation and testing of various types of algorithms that are deployed either at the level of the flight management unit (e.g., algorithms for UAV command and control), on the companion computer (e.g., algorithms used to track, to identify or to classify different targets), or deployed on the base station. This open development platform requires full access to the source code of all these: the flight management unit, the base station, and the embedded system. Moreover, the hardware components and how they communicate must be specially designed to be easily replaceable with ones specially adapted to the application requirements. Also, the paper proposes new innovative and inexpensive solutions to have a better, and a more reliable system able to work as much as closely to real environments (e.g., an urban one).
Radar emitter signal recognition based on convolutional neural network and main ridge coordinate transformation of ambiguity function

Aiming at the time-consuming and inconspicuous features of artificially extracting radar emitter signal features, a recognition method based on deep learning convolutional neural network and ambiguity function main ridge coordinate transformation is proposed. The method extracts the main ridge of the ambiguity function of the signal through fast discrete fractional Fourier transform, and then uses the two-dimensional time-frequency diagram of the main ridge polar coordinate domain of the ambiguity function as the input of the convolutional neural network to realize the sorting and recognition of different radar signals. Experiments show that the method in this paper not only maintains a 100% recognition rate above 0dB, but also stabilizes the recognition accuracy rate above 90% at -6dB. Compared with traditional radar signal recognition methods and other deep learning model recognition methods, the proposed method has greatly improved recognition rate and robustness, and has certain engineering application value.
Impacts towards a comprehensive assessment of the book impact by integrating multiple evaluation sources

The surge in the number of books published makes the manual evaluation methods difficult to efficiently evaluate books. The use of books' citations and alternative evaluation metrics can assist manual evaluation and reduce the cost of evaluation. However, most existing evaluation research was based on a single evaluation source with coarse-grained analysis, which may obtain incomprehensive or one-sided evaluation results of book impact. Meanwhile, relying on a single resource for book assessment may lead to the risk that the evaluation results cannot be obtained due to the lack of the evaluation data, especially for newly published books. Hence, this paper measured book impact based on an evaluation system constructed by integrating multiple evaluation sources. Specifically, we conducted finer-grained mining on the multiple evaluation sources, including books' internal evaluation resources and external evaluation resources. Various technologies (e.g. topic extraction, sentiment analysis, text classification) were used to extract corresponding evaluation metrics from the internal and external evaluation resources. Then, Expert evaluation combined with analytic hierarchy process was used to integrate the evaluation metrics and construct a book impact evaluation system. Finally, the reliability of the evaluation system was verified by comparing with the results of expert evaluation, detailed and diversified evaluation results were then obtained. The experimental results reveal that differential evaluation resources can measure the books' impacts from different dimensions, and the integration of multiple evaluation data can assess books more comprehensively. Meanwhile, the book impact evaluation system can provide personalized evaluation results according to the users' evaluation purposes. In addition, the disciplinary differences should be considered for assessing books' impacts.
Conditional pre‐trained attention based Chinese question generation

Summary Question generation is a promising and important area in natural language processing. According to the information of the given text, the question generation model can automatically generate a variety of questions, which are conducive to processing various subsequent tasks. Chinese question generation is a specific sub‐area of question generation. Due to the characteristics of Chinese question generation tasks, many methods are not suitable for it, and the generated results are either incorrect in word order or invalid expression. In order to address such challenging problem, we propose the conditional pre‐trained attention model termed A Lite BERT Conditional Question Generation (ALBERT‐CQG) for Chinese question generation. Through introducing general background knowledge of the pre‐trained model and conditional information of the given answers, this model has capacity of generating more valid expression. To our knowledge, we are the first to apply the conditional pre‐trained attention model to Chinese question generation tasks. The experimental results on two known benchmark datasets of Chinese question answering show that the ALBERT‐CQG outperforms its recent peers.
Minimum error entropy criterion‐based randomised autoencoder

The extreme learning machine-based autoencoder (ELM-AE) has attracted a lot of attention due to its fast learning speed and promising representation capability. However, the existing ELM-AE algorithms only reconstruct the original input and generally ignore the probability distribution of the data. The minimum error entropy (MEE), as an optimal criterion considering the distribution statistics of the data, is robust in handling non-linear systems and non-Gaussian noises. The MEE is equivalent to the minimisation of the Kullback–Leibaler divergence. Inspired by these advantages, a novel randomised AE is proposed by adopting the MEE criterion as the loss function in the ELM-AE (in short, the MEE-RAE) in this study. Instead of solving the output weight by the Moore–Penrose generalised inverse, the optimal output weight is obtained by the fixed-point iteration method. Further, a quantised MEE (QMEE) is applied to reduce the computational complexity of. Simulations have shown that the QMEE-RAE not only achieves superior generalisation performance but is also more robust to non-Gaussian noises than the ELM-AE.
Robust estimation algorithm based on prior probability statistics

Abstract For the nonlinear systems with uncertain system parameters, based on the recursive linear minimum variance estimation framework, the effect of the system parameters on the mean squared error (MSE) is analyzed, that is, the greater the system parameters deviation (whether they are positive or negative deviation) is, the greater the MSE is. Based on the analysis results and prior probability statistics (probability distributions or sampling distributions), an innovative robust estimation algorithm is proposed for nonlinear systems with uncertain system parameters. Three examples verify the effectiveness of the proposed algorithm.
Efficient Constructions of Pairing Based Accumulators

Cryptographic accumulators are a crucial building block for a variety of applications where you need to represent a set of elements in a compact format while still being able to provide proofs of (non)membership. In this work, we give a number of accumulator constructions for the bilinear pairing setting in the trapdoor-based scenario, where a trusted manager maintains the accumulator. Using modular accumulator techniques, we first present the first optimally efficient (in terms of communication cost) dynamic, positive accumulators in the pairing setting. Additionally, we present a novel modular approach to construct universal accumulators that avoid costly non-membership proofs. We instantiate our generic construction and present the first universal accumulator in the bilinear pairing setting, that achieves constant parameter size, constant cost for element additions/deletions and witness generation by the manager, constant witness updates by the users and constant (non)membership verification. We finally show how our proposed universal accumulator construction can give rise to efficient ZK accumulators with constant non-membership witness updates.
Challenges in Requirement Management Process: An Overview

Most researchers, if not all, would agree that regardless of software development methodology used for constructing software, the need to deal with requirements is real. Multiple studies have highlighted that erroneous in requirements is one of the major contributions for unsuccessful software development process (SDP). Hence, this paper intends to share findings from Structured Literature Review (SLR) that have been conducted, which studies some of the challenges arise in requirement management which could be seen as negatively impacting the requirement management (RM) process in context of SDP. Two main databases, i.e., Scopus and ScienceDirect have been used for extracting relevant papers dated between 2015 and 2021. From the result of frequency analysis performed on the papers, nine challenging activities are identified; with the top five being requirement engineering; the knowledge transfer between process; response toward changes; maintaining communication between stakeholders; and documentation. With the findings, it can be said that, whilst requirement management is an established activity, there are still factors that may contribute to failure if not properly attended.
Research on the state detection of the secondary panel of the switchgear based on the YOLOv5 network model

Abstract In recent years, with the development of artificial intelligence algorithms, deep learning algorithms are widely used in target detection. The switchgear in the power system plays a key role in the safe operation of the system and there are a large number of them. In order to improve the work efficiency of testers and reduce manual misjudgment, it is proposed to apply the deep learning YOLOv5 algorithm to detect the status of the switchgear secondary panel in real time. The algorithm uses the configuration environment, The data set training and target test obtain the light state information, and the experimental results prove the effectiveness of the algorithm, which provides technical support for the field of switchgear electrical test robots.