Reviewerly/exHarmony huggingface dataset:
Toward Unsupervised Graph Neural Network: Interactive Clustering and Embedding via Optimal Transport

Most of the existing Graph Neural Networks (GNNs) are deliberately designed for semi-supervised learning tasks, where supervision information (labelled node) is utilized to mitigate the oversmoothing problem of message passing. Unfortunately, the oversmoothing problem tends to be more severe in unsupervised tasks, since supervision information is not available. Since community structure/cluster is an essential characteristic of network, a natural approach to reduce the oversmoothing problem is to also constrain the node embeddings to maintain their own characteristics to prevent all the node embeddings from becoming too similar to be distinguished. In this paper, a novel Optimal Transport based Graph Neural Network (OT-GNN) is proposed to overcome the oversmoothing problem in unsupervised GNNs by imposing the equal-sized clustering constraints to the obtained node embeddings. To solve the combinatorial optimization problem, the constrained objective function of unsupervised GNN is relaxed to an Optimal Transport problem, and a fast version of the Sinkhorm-Knopp algorithm is adopted to handle large networks. Extensive experiments on node clustering and classification demonstrate the superior performance of our proposed OT-GNN.
Attention-Based Deep Metric Learning for Near-Duplicate Video Retrieval

Near-duplicate video retrieval (NDVR) is an important and challenging problem due to the increasing amount of videos uploaded to the Internet. In this paper, we propose an attention-based deep metric learning method for NDVR. Our method is based on well-established principles: We leverage two-stream networks to combine RGB and optical flow features, and incorporate an attention module to effectively deal with distractor frames commonly observed in near duplicate videos. We further aggregate the features corresponding to multiple video segments to enhance the discriminative power. The whole system is trained using a deep metric learning objective with a Siamese architecture. Our experiments show that the attention module helps eliminate redundant and noisy frames, while focusing on visually relevant frames for solving NVDR. We evaluate our approach on recent large-scale NDVR datasets, CC_WEB_VIDEO, VCDB, FIVR and SVD. To demonstrate the generalization ability of our approach, we report results in both within- and cross-dataset settings, and show that the proposed method significantly outperforms state-of-the-art approaches.
Dog Breed Classifier for Facial Recognition using Convolutional Neural Networks

This paper dealt with the breed classification of dogs. To classify dog breed is a challenging part under a deep convolutional neural network. A set of sample images of a breed of dogs and humans are used to classify and learn the features of the breed. The images are converted to a single label of dimension with image processing. The images of human beings and dogs are considered for breed classification to find the existing percentage of features in humans of dogs and dogs of human. This research work has used principal component analysis to shorten the most similar features into one group to make an easy study of the features into the deep neural networks. And, the facial features are stored in a vector form. This prepared vector will be compared with each feature of the dog into the database and will give the most efficient result. In the proposed experiment, 13233 human images and 8351 dog images are taken into consideration. The images under test are classified as a breed with the minimum weight between test and train images. This paper is based on research work that classifies different dogs breed using CNN. If the image of a dog is supplied then the algorithm will work for finding the breed of dog and features similarity in the breed and if the human image is supplied it determines the facial features existing in a dog of human and vice-versa.
A training algorithm with selectable search direction for complex-valued feedforward neural networks

This paper focuses on presenting an efficient training algorithm for complex-valued feedforward neural networks by utilizing a tree structure. The basic idea of the proposed algorithm is that, by introducing a set of direction factors, distinctive search directions are available to be selected at each iteration such that the objective function is reduced as much as possible. Compared with some well-known training algorithms, one of the advantages of our algorithm is that the determination of search direction is of great flexibility and thus more accurate solution is obtained with faster convergence speed. Experimental simulations on pattern recognition, channel equalization and complex function approximation are provided to verify the effectiveness and applications of the proposed algorithm.
SaaS - Microservices-Based Scalable Smart Contract Architecture

Existing blockchain smart contract platforms are designed as monolithic architectures. Even though there are multiple smart contracts with fully independent business logic, they run on a single monolithic container. This dependence on a monolithic container can be a performance bottleneck during the processing of a large number of transactions. To address this challenge, microservice-based architecture is adopted in the blockchain smart contracts by introducing a novel architecture to run independently on separate microservices. The new smart contract architecture is built on top of Mystiko blockchain, a functional programming and actor-based “Aplos” concurrent smart contract platform. Aplos is identified as a “Smart Actor” platform since it is built using Actor-based concurrency handling. Based on the philosophy of microservices, the Aplos Smart Actor platform on Mystiko blockchain is redesigned. This architecture is introduced as “SaaS - Smart actors as a service”. With SaaS, different Aplos smart actors in the blockchain are deployed as separate independent services (e.g. docker containers) instead of a single monolith service. This ensures different smart actors can execute transactions independently. An additional benefits to SaaS is that the architecture increases the scalability by guaranteeing concurrent execution of transactions, producing high transaction throughput on the blockchain.
Systematic Literature Review Of Hate Speech Detection With Text Mining

Along with the increasing activity on social media, hate speech is getting out of control. Hate speech detection can be done by utilizing text mining technology. There have been many hate speech detection studies conducted. To identify and analyze research trends, data sources, methods and features used in hate speech detection, this systematic literature review was created. Until early 2020, the topics of hate speech were found, including hate speech against minorities, religion, women, the general election agenda, and politics. Sources of data that are widely used to be used as datasets come from twitter. Hate speech is not only classified into HS (hate speech) and Non-HS (non-hate speech) but can be further classified into racism, sexism, offensive, abusive, threats of violence and others. Of the 38 studies that meet inclusion and exclusion, there are 26 algorithms and 28 features that have been used to detect hate speech. However, these methods and features do not necessarily guarantee a good hate detection performance. Hate speech classification performance is also influenced by the dataset, the features chosen, the number of classes and mutually exclusive classes.
Selection of CNN, Haralick and Fractal Features Based on Evolutionary Algorithms for Classification of Histological Images

The analysis of histological image features for automatic detection of pathologies plays an important role in medicine. Considering that, we proposed a method based on the association of features extracted by multi-scale and multidimensional fractal techniques, Haralick descriptors, and CNN for pattern recognition of colorectal cancer, breast cancer, and non-Hodgkin lymphomas. For feature selection, we applied the ReliefF algorithm to rank the best 50 features and then applied the evolutionary algorithms GWO, PSO, and GA. The classification was made with SVM, K*, and Random Forest algorithms. This strategy allows classifying plenty of feature vectors selected by different algorithms, and consequently, improves the accuracy of the interpretations about the class distinction of histological images. The best combination found was composed of GA and K* algorithms, resulting in 91.06%, 90.52% e 82.01% accuracy for colorectal cancer, breast cancer, and non-Hodgkin lymphomas respectively. The performance obtained by the method indicates that the feature association extracted by different approaches and their subsequent selection and classification presents a potential field for further studies with a high degree of contribution to science.
Integrated Path Planning for Unmanned Differential Steering Vehicles in Off-Road Environment With 3D Terrains and Obstacles

The path planning of unmanned differential steering vehicles (UDSVs) in the off-road environment not only needs to consider the non-complete constraints of vehicles but also faces the challenges of complex off-road terrains and obstacles. In this paper, an integrated path planning system is proposed to handle the influence of kinematic vehicle model, off-road terrains and obstacles systematically for the path planning of UDSVs. To improve the planning efficiency, a Pre-planning is designed and carried out using the Voronoi diagram established in the 3D environment with obstacles. By combining the potential field functions (PFF) related to passable obstacles and 3D terrains, an integrated PFF is defined to represent the movement cost of UDSV in the nonlinear optimal control (NOC) problem. Based on the NOC, a channel path planning (CPP) problem is formulated to avoid the untraceable path caused by the traditional line path planning (LPP). Simulation results show that the proposed system can plan a feasible path fast with the constraints from vehicle kinematics, obstacle avoidance and off-road terrains.
Proximity Gaps for Reed–Solomon Codes

A collection of sets displays a proximity gap with respect to some property if for every set in the collection, either (i) all members are δ-close to the property in relative Hamming distance or (ii) only a tiny fraction of members are δ-close to the property. In particular, no set in the collection has roughly half of its members δ-close to the property and the others δ-far from it. We show that the collection of affine spaces displays a proximity gap with respect to Reed-Solomon (RS) codes, even over small fields, of size polynomial in the dimension of the code, and the gap applies to any δ smaller than the Johnson/Guruswami-Sudan list-decoding bound of the RS code. We also show near-optimal gap results, over fields of (at least) linear size in the RS code dimension, for δ smaller than the unique decoding radius. Concretely, if δ is smaller than half the minimal distance of an RS code V ⊂ Fq <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , every affine space is either entirely δ-close to the code, or alternatively at most an ( n/q)-fraction of it is δ-close to the code. Finally, we discuss several applications of our proximity gap results to distributed storage, multi-party cryptographic protocols, and concretely efficient proof systems. We prove the proximity gap results by analyzing the execution of classical algebraic decoding algorithms for Reed-Solomon codes (due to Berlekamp-Welch and Guruswami-Sudan) on a formal element of an affine space. This involves working with Reed-Solomon codes whose base field is an (infinite) rational function field. Our proofs are obtained by developing an extension (to function fields) of a strategy of Arora and Sudan for analyzing low-degree tests.
Static Profiling and Optimization of Ethereum Smart Contracts Using Resource Analysis

Profiling tools have been widely used for studying the behavior of the programs with the objective of reducing the amount of resources consumed by them. Most profilers collect the information with dynamic techniques, i.e., execute an instrumented version of the program with some specific input arguments to profile the measures of interest. This article presents a novel static profiling technique for Ethereum smart contracts that, using static resource analysis, is able to generate upper-bound expressions that can be used to produce profiling information about the measure of interest. Unlike traditional profiling tools, we get upper-bounds on the measures of interest expressed in terms of the input arguments or the state variables of the smart contracts. The information that can be obtained by the upper-bounds allows us to detect gas-expensive fragments of a Solidity program or to spot resource-related vulnerabilities at specific program points of the program. Moreover, in this article we propose an automatic optimization of Solidity programs which reduces their gas consumption replacing the accesses to state variables by gas-efficient accesses to local variables. We have experimentally evaluated our technique and we have detected that 6.81% of the public functions analyzed can be optimized and 1.43% are vulnerable to execute arbitrary code.
Automatic Registration of Remote Sensing Images Based on Revised SIFT With Trilateral Computation and Homogeneity Enforcement

Automatic registration of remote sensing images is an essential task that requires the establishment of appropriate correspondences between the sensed image and the reference image. Among the feature-based matching approaches, attempts relied on the scale-invariant feature transform (SIFT) algorithm have shown particular superiority over other methods. From the perspective of automatic registration, the challenges of SIFT-based methods involve the elimination of mismatches and the homogeneity of matches. While many outlier removal methods have been proposed, the strategies for uniform distribution have been lacking. To address these two issues, this article investigates a new remote sensing image registration algorithm based upon a revised SIFT scheme. Additionally, an outlier removal mechanism founded on a trilateral computation (Tc) recipe and a homogeneity enforcement (He) layout according to a divide-and-conquer inclusion tactic are proposed. Finally, a stochastic competition process based upon game theory is introduced to secure an appropriate amount of correct matches in the proposed Tc and He (TcHe)-SIFT framework. A wide variety of multispectral and multitemporal remote sensing images with various scenarios were exploited to evaluate the proposed registration algorithm. Experimental results demonstrated the advantages of our developed remote sensing image registration algorithm over the state-of-the-art SIFT-based methods. We believe that this new registration technique is of potential in a number of remote sensing image processing applications.
Vocal Tract Length Perturbation for Text-Dependent Speaker Verification With Autoregressive Prediction Coding

In this letter, we propose a vocal tract length (VTL) perturbation method for text-dependent speaker verification (TD-SV), in which a set of TD-SV systems are trained, one for each VTL factor, and score-level fusion is applied to make a final decision. Next, we explore the bottleneck (BN) feature extracted by training deep neural networks with a self-supervised objective, autoregressive predictive coding (APC), for TD-SV and compare it with the well-studied speaker-discriminant BN feature. The proposed VTL method is then applied to APC and speaker-discriminant BN features. In the end, we combine the VTL perturbation systems trained on MFCC and the two BN features in the score domain. Experiments are performed on the RedDots challenge 2016 database of TD-SV using short utterances with Gaussian mixture model-universal background model and i-vector techniques. Results show the proposed methods significantly outperform the baselines.
Silhouette: Efficient Cloud Configuration Exploration for Large-Scale Analytics

Choosing the best cloud configuration for large-scale data analytics jobs deployed in the cloud can substantially improve their performance and reduce costs. However, current cloud providers offer a wide variety of instance types and customized cluster sizes, making it both time-consuming and costly to pinpoint the optimal cloud configuration. This article presents the design, implementation, and evaluation of Silhouette, a cloud configuration selection framework based on performance models for various large-scale analytics jobs with minimal training overhead. The essence of Silhouette is to build performance prediction models with carefully selected small-scale experiments on small subsets of input data to estimate the performance with entire input data on larger cluster sizes. To reduce the training time and cost, Silhouette incorporates new statistical techniques to select those experiments that yield the best possible information for performance prediction. Moreover, we develop a novel model transformer to convert a prediction model built on one instance type to a different instance type with only one extra experiment, which significantly reduces the training overhead. We evaluate Silhouette with an extensive array of large-scale data analytics jobs on Amazon EC2. Our experimental results have shown convincing evidence that Silhouette is effective in optimizing cloud configuration while saving both training time and costs compared with existing solutions.
Multiscale Detection of Circles, Ellipses and Line Segments, Robust to Noise and Blur

This paper proposes a basic taxonomy of image contours. Our goal is to classify smooth curves into five categories, namely, circles, ellipses, line segments, arcs of circles and arcs of ellipses. These geometrical structures have been chosen as they serve as input of many computer vision tasks. The proposed strategy is applied on a set of initial disjoint contours, which are grouped together to form the aforementioned structures. These, in turn, are validated using an a contrario approach that guarantees a reduced number of false detections. The use of a multiscale strategy permits the detection at different resolution levels, which makes the method robust to noise and blur.
Comparing the results of replications in software engineering

It has been argued that software engineering replications are useful for verifying the results of previous experiments. However, it has not yet been agreed how to check whether the results hold across replications. Besides, some authors suggest that replications that do not verify the results of previous experiments can be used to identify contextual variables causing the discrepancies. Study how to assess the (dis)similarity of the results of SE replications when they are compared to verify the results of previous experiments and understand how to identify whether contextual variables are influencing results. We run simulations to learn how different ways of comparing replication results behave when verifying the results of previous experiments. We illustrate how to deal with context-induced changes. To do this, we analyze three groups of replications from our own research on test-driven development and testing techniques. The direct comparison of p-values and effect sizes does not appear to be suitable for verifying the results of previous experiments and examining the variables possibly affecting the results in software engineering. Analytical methods such as meta-analysis should be used to assess the similarity of software engineering replication results and identify discrepancies in results. The results achieved in baseline experiments should no longer be regarded as a result that needs to be reproduced, but as a small piece of evidence within a larger picture that only emerges after assembling many small pieces to complete the puzzle.
Classifying and completing word analogies by machine learning

Analogical proportions are statements of the form 'a is to b as c is to d', formally denoted a:b::c:d. They are the basis of analogical reasoning which is often considered as an essential ingredient of human intelligence. For this reason, recognizing analogies in natural language has long been a research focus within the Natural Language Processing (NLP) community. With the emergence of word embedding models, a lot of progress has been made in NLP, essentially assuming that a word analogy like man:king::woman:queen is an instance of a parallelogram within the underlying vector space. In this paper, we depart from this assumption to adopt a machine learning approach, i.e., learning a substitute of the parallelogram model. To achieve our goal, we first review the formal modeling of analogical proportions, highlighting the properties which are useful from a machine learning perspective. For instance, the postulates supposed to govern such proportions entail that when a:b::c:d holds, then seven permutations of a,b,c,d still constitute valid analogies. From a machine learning perspective, this provides guidelines to build training sets of positive and negative examples. Taking into account these properties for augmenting the set of positive and negative examples, we first implement word analogy classifiers using various machine learning techniques, then we approximate by regression an analogy completion function, i.e., a way to compute the missing word when we have the three other ones. Using a GloVe embedding, classifiers show very high accuracy when recognizing analogies, improving state of the art on word analogy classification. Also, the regression processes usually lead to much more successful analogy completion than the ones derived from the parallelogram assumption.
An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians

We consider solving nonlinear optimization problems with a stochastic objective and deterministic equality constraints. We assume for the objective that its evaluation, gradient, and Hessian are inaccessible, while one can compute their stochastic estimates by, for example, subsampling. We propose a stochastic algorithm based on sequential quadratic programming (SQP) that uses a differentiable exact augmented Lagrangian as the merit function. To motivate our algorithm design, we first revisit and simplify an old SQP method Lucidi (J. Optim. Theory Appl. 67(2): 227–245, 1990) developed for solving deterministic problems, which serves as the skeleton of our stochastic algorithm. Based on the simplified deterministic algorithm, we then propose a non-adaptive SQP for dealing with stochastic objective, where the gradient and Hessian are replaced by stochastic estimates but the stepsizes are deterministic and prespecified. Finally, we incorporate a recent stochastic line search procedure Paquette and Scheinberg (SIAM J. Optim. 30(1): 349–376 2020) into the non-adaptive stochastic SQP to adaptively select the random stepsizes, which leads to an adaptive stochastic SQP. The global "almost sure" convergence for both non-adaptive and adaptive SQP methods is established. Numerical experiments on nonlinear problems in CUTEst test set demonstrate the superiority of the adaptive algorithm.