Reviewerly/exHarmony huggingface dataset:
Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments

In hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific workflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the dynamics of hybrid cloud environments, the data transmission time is uncertain. Thus, it poses huge challenges to the efficient data placement for scientific workflows. However, most of the traditional solutions for data placement focus on deterministic cloud environments, which lead to the excessive data transmission time of scientific workflows. To address this problem, we propose an adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operators (DPSO-FGA) to minimize the fuzzy data transmission time of scientific workflows. The DPSO-FGA can rationally place the scientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. Simulation results show that the DPSO-FGA can effectively reduce the fuzzy data transmission time of scientific workflows in hybrid cloud environments.
A Perturbed Particle Swarm Optimization using Harmony Search and Mutation

This paper applies the harmony search (HS) and mutation to lessens the stagnant of PSO. When a particle stops improving, it is mutated or replace by a position value created from harmony memory, which memorized improve locations of particles (Pbest) in search space. Another HS generates the best position among all particles (Gbest) to replace stagnant Gbest to sway the swarm from the trapping location. This HS has another harmony memory that contains improving Gbest. The results of the proposed algorithm are compared with related modified-PSO using 27 benchmark functions. The proposed algorithm locates optimum in more benchmark functions with faster execution.
Trace Ratio Optimization for High-Dimensional Multi-Class Discrimination

In multi-class discrimination with high-dimensional data, identifying a lower-dimensional subspace with maximum class separation is crucial. We propose a new optimization criterion for finding such a discriminant subspace, which is the ratio of two traces: the trace of between-class scatter matrix and the trace of within-class scatter matrix. Since this problem is not well-defined for high-dimensional data, we propose to regularize the within trace and maximize the between trace. A careful investigation reveals that this optimization has an innate connection to the eigenvalue decomposition of an indefinite matrix. For the sake of better interpretability of the classifier, we also consider a sparse estimation via a group-wise soft-thresholding. Interesting relationships between the proposed method and some classical methods such as Fisher’s linear discriminant analysis and its variants are discussed. Empirical examples with simulated and real datasets suggest that the proposed method works well and is often better than some existing approaches in a wide range of problems, with respect to both variable selectivity and classification accuracy. Supplementary files for this article are available online.
Faster Glare Detection on Document Images

Glare on images is caused by a bright light source or its reflection. This effect can hide or corrupt important information on a document image, negatively affecting the overall quality of text recognition and data extraction. In this paper, we present a new method for glare detection which is based on our prior work on barcode detection, where we used a convolutional neural network (CNN) with consecutive downsampling and context aggregation modules to extract relevant features. In this paper, we propose a similar CNN to create a segmentation map for image glare. The proposed model is fast, lightweight, and outperforms previous approaches in both inference time and quality. We also introduce a new dataset of 687 document images with glare. The glare regions on the document images were marked and the dataset itself has been made publicly available. All of the measurements and comparisons discussed in this paper were performed using this dataset. We managed to obtain an $$F_1$$ -measure of 0.812 with an average run time of 24.3 ms on an iPhone XS. The experimental data is available at https://github.com/RodinDmitry/glare .
MG-BERT: A Multi-glosses BERT Model for Word Sense Disambiguation

Word Sense Disambiguation (WSD) is a core task in NLP fields and has many potential applications. Traditional supervised methods still have obstacles, such as the problem of variable size of label candidates and the lack of annotated corpora. Although attempts are made to integrate gloss information to the model, no existing models have paid attention to the divergences among glosses. In this paper, we propose a Multi-Glosses BERT (MG-BERT) model with two main advantages for WSD task. Our model jointly encodes the context and multi-glosses of the target word. We show that our Context with Multi-Glosses mechanism can find out and emphasize the divergences among glosses and generate nearly orthogonal gloss embeddings, which makes it more accuracy to match the context with the correct gloss. We design three classification algorithms, Gloss Matrix Classifier (GMC), General Gloss Matrix Classifier (GGMC) and Space Transforming Classifier (STC), all of which can disambiguate words with full-coverage of WordNet. In GMC and GGMC, we utilize gloss embeddings as weight matrix. For STC, we transform different label space to a same label space. Experiment shows that our MG-BERT model achieves new state-of-the-art performance on all WSD benchmarks.
Thoughts Raised by 3 Alpha

The game is directly related to the study of decision-making support, and is one of the main contents of artificial intelligence (AI). The changes in playing Go are the most complex, so Go is considered the most challenging game in AI. AlphaGo was the first computer Go to beat the human Go champion, and we call the subsequent AlphaGo Zero, Alpha Zero and it 3a. Their emergence is a great step in AI and an important milestone in the history of AI, which has also raised some questions that are worth thinking. In this paper, after describing the main characteristics of 3a, we briefly comment on 3a, such as its adscription of the success, whether they have intelligence, their research level and so on. Some cross-domain problems caused by 3a are considered, such as the symbolization of inductive logic, the new issues of distributed system, the intension of AI, how to implement intelligence, artificial brain, and so on. In addition, we also briefly introduce the conceptual structure of deep learning, the use method of tensor processing unit (TPU) in 3a and the main instructions of TPU.
Optimizing All-to-All Data Transmission in WANs

All-to-all data transmission is a typical data transmission pattern in both consensus protocols and blockchain systems. Developing an optimization scheme that provides high throughput and low latency data transmission can significantly benefit the performance of those systems. This paper investigates the problem of optimizing all-to-all data transmission in a wide area network (WAN) using overlay multicast. We prove that in a hose network model, using shallow tree overlays with height up to two is sufficient for all-to-all data transmission to achieve the optimal throughput allowed by the available network resources. Upon this foundation, we build ShallowForest, a data plane optimization for consensus protocols and blockchain systems. The goal of ShallowForest is to improve consensus protocols' resilience to skewed client load distribution. Experiments with skewed client load across replicas in the Amazon cloud demonstrate that ShallowForest can improve the commit throughput of the EPaxos consensus protocol by up to 100% with up to 60% reduction in commit latency.
Natural Language Processing and Deep Learning for Blended Learning as an Aspect of Computational Linguistics

Machine learning has been used for several years already in information science to create algorithms which enable computers to solve problems without giving them particular tasks and instructions to perform these tasks. This paper attempts to concentrate on possibilities of this AI (artificial intelligence) subfield inasmuch it could prove helpful for blended learning. It brings possible questions which are connected to e-learning, blended learning and machine learning and its utilization in university courses. It also brings several questions of computational linguistics which could prove extremely helpful in blended learning processes in which it could bring big data analysis. These phenomena are relatively new, therefore, neglected by blended learning scholars, thus this paper brings these new ideas together and wants to present new ideas and concepts which will be utilized in blended learning area. It also suggests new approach to blended learning, coined by the term blended learning 2.0, which implements modern approaches such as computational linguistics and corpus linguistics into the utilization of e-platforms in the educational process.
GAN-based AI Drawing Board for Image Generation and Colorization

We propose a GAN(Generative Adversarial Networks)-based drawing board which takes the semantic (by segmentation) and color tone (by strokes) inputs from users and automatically generates paintings. Our approach is built on a novel and lightweight feature embedding which incorporates the colorization effects into the painting generation process. Unlike the existing GAN-based image generation models which take semantics input, our drawing board has the ability to edit the local colors after generation. Our method samples the color information from users’ strokes as extra input, then feeds it into a GAN model for conditional generation. We enable the creation of pictures or paintings with semantics and color control in real-time.
Concept-Enhanced Multi-view Clustering of Document Data

Many works implemented multi-view clustering algorithms in document clustering. One challenging problem in document clustering is the similarity metric. Existing multi-view document clustering methods widely used two measurements: the Cosine similarity and the Euclidean Distance (ED). The first did not consider the magnitude between the two vectors. The second cannot compute the dissimilarity of two vectors that share the same ED. In this paper, we proposed a multi-view document clustering scheme to overcome these drawbacks by calculating the heterogeneity between documents with the same ED while taking into consideration their magnitudes. The experimental results show that the proposed similarity function can measure the similarity between documents more accurately than the existing metrics, and the proposed document clustering scheme goes beyond the limit of several state-of-the-art algorithms.
Self-attention Networks for Speaker Identification with Negative-Focused Triplet Loss

Abstract In this paper, a model based on self-attention for text-independent speaker identification task is proposed. Traditional CNNs are expert at capturing local features but are inefficient when capturing long-range dependencies. The self-attention helps to capture long-range dependency more efficiently and requires less parameters. Besides, based on Triplet Loss and inspired by Weighted Triplet Loss, we propose a novel loss function for speaker identification task, named Negative-Focused Triplet Loss, which makes the training process more efficient and effective. To evaluate the performance of our model, the experiments are conducted on Voxceleb dataset, and we achieve Top-1 accuracy of 90.3% and Top-5 accuracy of 96.9%, which are competitive with the previous state-of-the-art performance. Without any hard sample selecting operation, we achieve better result than the baseline method that uses Cluster-Range Loss or Triplet Loss, which demonstrates the high efficiency of our proposed approach.
Agile systems engineering for critical systems

Participants in our 2020 Vector survey observe three significant challenges [1]. Cost and efficiency have emerged as the single most relevant short-term challenge, indicating the need to succeed in a fast-changing world with unclear business drivers. At the same time, quality is further growing compared to our last-year survey as short- and mid-term target.
Error prediction and structure determination for <scp>CMAC</scp> neural network based on the uniform design method

Abstract Insufficient study on error bound of cerebellar model articulation controller (CMAC) severely limits its application. To investigate the error prediction and structure determination of CMAC for multi‐dimensional and data‐generation objects, this paper builds a 10‐input 2‐output model for a desulfurization system to test 44,640 sets of operation data. Four test groups and one prediction group are designed and performed based on uniform design method. Regression analysis and curve fitting methods are applied for error analyses. The focus of regression analysis method is the influence of uniform table's level on its prediction formulas' accuracies, whereas curve fitting's is the impact of theoretical memory space (location number) and actual storage space (address number) of CMAC on output error. Based on the results, the following conclusions are obtained. (a) The prediction accuracy of the linear regression equation is not monotonous with the level of the uniform design table, but there is a distinct region with local high precision. (b) Compared with regression analysis and address number fitting methods, location number analysis method has distinct advantages of prediction range, accuracy and flexibility. (c) In terms of location number analysis, different intervals may correspond to different optimal fitting functions, but only power function maintains high prediction accuracy in the whole range where the method works. Besides, the scope of location number analysis is also studied, of which the lower borders are 10 9 and 10 10 approximately for model's two outputs, respectively.
Fractal Models and Algorithms for Creating a Protective Marking for Integrity and Authenticity Bitmap Images

Methods and algorithms for embedding marked information with fractal properties to ensure the integrity and authenticity of raster images are considered. The features of using algebraic fractals as intermediate cover images, as well as secret keys that define places for embedding watermarks, are analyzed. Numerical examples show that the proposed methods increase the integrity and authenticity of bitmap images.
Social Recommendation with Characterized Regularization

Social recommendation, which utilizes social relations to enhance recommender systems, has been gaining increasing attention recently with the rapid development of online social networks. Existing social recommendation methods are based on the assumption, so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">social-trust</i> , that users’ preference or decision is influenced by their social-connected friends’ purchase behaviors. However, they assume that the influences of social relationships are always the same, which violates the fact that users are likely to share preference on different products with different friends. More precisely, friends’ behaviors do not necessarily affect a user’s preferences, and the influence is diverse among different items. In this paper, we contribute a new solution, CSR (short for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> haracterized <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> ocial <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> egularization) model by designing a universal regularization term for modeling variable social influence. This regularization term captures the finely grained similarity of social-connected friends. We further introduce two variants of our model with different optimization manners. Our proposed model can be applied to both explicit and implicit interaction due to its high generality. Extensive experiments on three real-world datasets demonstrate that our CSR can outperform state-of-the-art social recommendation methods. Further experiments show that CSR can improve recommendation performance for those users with sparse social relations or behavioral interactions.
EDGAN: Disguising Text as Image using Generative Adversarial Network

In the concept of data hiding, image is often used as a cover to hide sensitive data inside it. This approach is considered a good addition in securing information to cryptography which only hides the information and not the presence of the message itself. The combination of Deep Learning with Steganography and Cryptography is rarely done. By utilizing Deep Neural Networks to encrypt and hide the messages, it will be increasingly difficult to decrypt and track.In this study, we developed an encryption mechanism to not only conceal messages, but transforming them into images. The image containing the hidden messages can later be decrypted and converted back into the original message. We use Generative Adversarial Network to develop the encryption and decryption models. Text data is converted into a word vector using word2vec model which then used as input for the encryption model to produce the word images. We use the MNIST dataset to train models which are able to produce images that encrypt 1000 word variations. Based on our experiments, we were able to produce robust encrypted images with 98% accuracy of reversible words. We also show that our model is resistant to various minor image attacks such as scaling, noise addition, and image rotation.
Proximal Support Vector Machine Classifier based on LMS Algorithm

In proximal support vector machine (PSVM), datapoints are assigned to classes by measuring the proximity of it from the two parallel hyperplanes. The hyperplanes are formed such that each of the two parallel hyperplanes is closest to only one of the datasets out of two datasets and the two parallel hyperplanes should be as far as possible from each other. The benefit of PSVM is that it demands less training time as the size of the training patterns increases and less computational complexity as compared to standard SVM. In this paper, an improved version of proximal support vector machine (PSVM) is proposed for pattern classification of binary datasets. In the proposed PSVM technique, the weight vector is modified based on least mean square (LMS) algorithm, which reduces the training classification error. The idea behind the proposed technique is to enlarge the separating boundary, such that the data points of each class lie on the correct side of the hyperplane and the separability between the datasets is increased. The performance of the proposed method is evaluated on several benchmark datasets for both linear and nonlinear classifiers. Experimental results show that the proposed LMS based proximal support vector machine (LMS-PSVM) classifier performs better compared to standard SVM and PSVM.
Development of Scrum-Tree-KNN Algorithm for Distributed Agile Development

Scrum is characterized by its developers as a structural platform applied to deal with intricate products and solutions which usually provide incorporation of various procedures or practices. The Scrum composition incorporates an iterative as well as, progressive methodology to optimize predictability and so risk control. As distributed agile development is planned for multi-national tasks we developed new algorithm for distributed agile development processes to lower the risk profile. The major aim of this algorithm is to decrease the pitfalls stemming in traditional software development life cycle. This paper signifies the newly developed scrum-tree- k-nearest neighbors algorithm algorithm for distributed agile development projects. The paper also presents the analysis of 15 agile projects.
Research on Diagnosis Method Based on Multi-Class Sample Imbalanced Data

In reality, data on many engineering problems are classified as imbalanced data, which poses significant challenges to adopting a data-driven approach. In this paper, the SOU data preprocessing method combining synthetic minority oversampling technique, random oversampling and random under sampling is used for data equalization. After, the cars evaluation, wine production regions are classified using the ensemble learning based on stacking method. Research is also conducted on the classification of imbalances in the condition assessment of aero-engines at work. The results show that the data equalization method and diagnostic framework proposed in this paper can effectively diagnose multi-class imbalanced data, and the ensemble model has higher diagnostic stability and generalization ability, which is more effective for solving multi-class imbalance problems.
Quantum system dynamics with a weakly nonlinear Josephson junction bath

We investigate the influence of a weakly nonlinear Josephson bath consisting of a chain of Josephson junctions on the dynamics of a small quantum system (LC oscillator). Focusing on the regime where the charging energy is the largest energy scale, we perturbatively calculate the correlation function of the Josephson bath to the leading order in the Josephson energy divided by the charging energy while keeping the cosine potential exactly. When the variation of the charging energy along the chain ensures fast decay of the bath correlation function, the dynamics of the LC oscillator that is weakly and capacitively coupled to the Josephson bath can be solved through the Markovian master equation. We establish a duality relation for the Josephson bath between the regimes of large charging and Josephson energies, respectively. The results can be applied to cases where the charging energy either is nonuniformly engineered or disordered in the chain. Furthermore, we find that the Josephson bath may become non-Markovian when the temperature is increased beyond the zero-temperature limit in that the bath correlation function gets shifted by a constant and does not decay with time.
Research on Clustering Algorithm Based on Web Log Mining

Abstract People’s online business behaviour has become more and more frequent. Internet service providers are beginning to find ways to obtain the interests and hobbies of users in order to provide targeted services to users. Analysis of user behavior based on Web logs can obtain valuable information from users. User clustering based on Web logs can cluster users according to user behavior, and then analyze user access patterns, providing a good solution for problem solving. This article introduces the concept and process of data mining, the classification and process of Web data mining, and then analyzes the K-Means clustering algorithm. The class-centered algorithm avoids clustering and only obtains the local optimal solution, and it can reduce the algorithm iteration time and improve the clustering quality.