Reviewerly/exHarmony huggingface dataset:
Adversarially regularized graph attention networks for inductive learning on partially labeled graphs

The high cost of data labeling often results in node label shortage in real applications. To improve node classification accuracy, graph-based semi-supervised learning leverages the ample unlabeled nodes to train together with the scarce available labeled nodes. However, most existing methods require the information of all nodes, including those to be predicted, during model training, which is not practical for dynamic graphs with newly added nodes. To address this issue, an adversarially regularized graph attention model is proposed to classify newly added nodes in a partially labeled graph. An attention-based aggregator is designed to generate the representation of a node by aggregating information from its neighboring nodes, thus naturally generalizing to previously unseen nodes. In addition, adversarial training is employed to improve the model's robustness and generalization ability by enforcing node representations to match a prior distribution. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art methods. The code is available at https://github.com/JiarenX/AGAIN.
Judgments of the European court of human rights v. The Republic of Serbia on the application of genetic testing in paternity litigation

The case law of the European Court of Human Rights is of great importance for the formulation of human rights standards as it applies the European Convention on Human Rights by interpreting the prescribed rights and freedoms taking into account social reality and legal regulation in Council of Europe member states. When joining this political organization the Republic of Serbia carried out the procedures of adjusting the legislation to the convention requirements, so that, in normative sense, Serbian family law systematically follows modern standards of human rights protection. The decisions of the Court in cases in which the issues of establishing paternity was applied by DNA analysis are conditioned by the circumstances of each individual case. In this paper reasearch is devoted to the two judgments of the European Court of Human Rights versus Republic of Serbia on determining the origin of the child from the father judgments that have a family law in the narrow sense in which Court took the position that domestic legislation did not take into account the relevant elements of the case, the possibility to establish a balance of relevant interests when determing the identity of the biological father regarding DNA analysis. By definition genetic testing implies the analysis of one genome and its products, its function or DNA or chromosomal analysis aimed at identifying or contradicting certain facts. This method involves comparing the DNA profile of a child with DNA profile of the potential father by comparing locus - specific gene location or DNA region on chromosome - which differ in their structure and length, so that non-blood person have different structure of the molecule in each analyzed locus, while biological relatives have the same structure. This means that their DNA profiles have visible traces of genetic heritage. Although every person has a vital interest in finding out information that complete his/her own knowledge of his/her background it is important to know that third party protection can prevent him/her from being forced into medical testing of any kind, including DNA analysis. Member States have different solutions to deal with in cases where a potential father refuses to undergo tests necessary to establish facts of a biological origin. In some jurisdictions non-compliance with medical testing is sanctioned by monetary or imprisonment penalty, while in others it is for the failure to act on a warrant the court activates the presumption of paternity. When paternity cannot be determined by DNA analysis, Member States must provide the determination of paternity by alternative means of evidence taking into account the existence of a fair balance between the right to know the origin and the right of potential father not to undergo this type of medical expertise.
NarGNN: Narrative Graph Neural Networks for New Script Event Prediction Problem

Scripts encode world knowledge that can help text understanding, and script learning is to automatically learn this knowledge from unstructured text. Previous script event prediction only considers predicting subsequent events given an existing event sequence. However, in real life, we want to predict events at any location rather than just the last event, because we can control subsequent events by preventing or promoting intermediate events. To this end, we introduce a novel approach, named NarGNN, which can integrate the factual knowledge and experiential knowledge to predict the intermediate event. Specifically, we first construct an event evolutionary graph from the newswire corpus. Then we use Fact Encoder Layer to encode existing event facts, including the semantic information of the event itself and the sequence information among events. Third, we use Fusion Layer to fuse the graph information containing experiential knowledge and the embeddings of existing facts obtained from the previous layer. Fourth, Attention Layer is used to choose the most reasonable result. Finally, our proposed model is evaluated on widely used New York Times corpus and the results demonstrate significant improvements compared with state-of-the-art methods. Also, it is worth noting that NarGNN can be naturally extended to address the previous task with better performances than other methods.
Design of Power Grid Data Asset Management System Based on Directory Blockchain

Grid enterprises started late in data management, development, and applications, due to the constraints of the industry, business structure and technical means. As an innovative application of both technical solutions and management means, blockchain is highly compatible with data asset management. This paper designs a directory blockchain system based on blockchain technology and power grid data resource directory, which records the source, use, sharing and other operations of power grid business data, so as to ensure that the ownership, location, content and other information cannot be tampered with. Three stages of application scenarios are designed to gradually expand the directory blockchain nodes and application scope from the inside to the outside, so as to upgrade the data governance level and promote open data sharing.
BOUNDED-ANALYTIC SEQUENT CALCULI AND EMBEDDINGS FOR HYPERSEQUENT LOGICS

Abstract A sequent calculus with the subformula property has long been recognised as a highly favourable starting point for the proof theoretic investigation of a logic. However, most logics of interest cannot be presented using a sequent calculus with the subformula property. In response, many formalisms more intricate than the sequent calculus have been formulated. In this work we identify an alternative: retain the sequent calculus but generalise the subformula property to permit specific axiom substitutions and their subformulas. Our investigation leads to a classification of generalised subformula properties and is applied to infinitely many substructural, intermediate, and modal logics (specifically: those with a cut-free hypersequent calculus). We also develop a complementary perspective on the generalised subformula properties in terms of logical embeddings. This yields new complexity upper bounds for contractive-mingle substructural logics and situates isolated results on the so-called simple substitution property within a general theory.
Integer Codes Correcting Asymmetric Errors in Nand Flash Memory

Memory devices based on floating-gate transistor have recently become dominant technology for non-volatile storage devices like USB flash drives, memory cards, solid-state disks, etc. In contrast to many communication channels, the errors observed in flash memory device use are not random but of special, mainly asymmetric, type. Integer codes which have proved their efficiency in many cases with asymmetric errors can be applied successfully to flash memory devices, too. This paper presents a new construction and integer codes over a ring of integers modulo A=2n+1 capable of correcting single errors of type (1,2),(±1,±2), or (1,2,3) that are typical for flash memory devices. The construction is based on the use of cyclotomic cosets of 2 modulo A. The parity-check matrices of the codes are listed for n≤10.
Backpropagation method modification using Taylor series to improve accuracy of offline neural network training

The aim of this research is to derive new, more effective equations to adjust parameters of a neural network, which have advantage over formulas of the backpropagation method. For this purpose, an objective function of the neural network training is proposed to be modified using expansion of the abstract ideal output of each network layer into a Taylor series. Such decomposition is to be implemented at the point, which is close to the current output of the layer under consideration. The modified objective function gradients are calculated with respect to the difference between ideal and real values of the network parameters. As a result, the obtained weights and biases adjustment equations (for each layer) depend not only on the output of the previous layer, but also on its derivatives. Comparing to the backpropagation method, they allow to reduce the offline neural network training time. The derived formulas can be reduced to the ones of the backpropagation method. This proves the correctness of their development. The efficiency of the new training equations is shown using MNIST handwritten character recognition task. Considering the multilayer structures with different activation functions and number of layers, as far as new formulas are concerned, the same recognition accuracy is achieved, but faster comparing to the conventional backpropagation.
Design and methodology of algorithm for the enhancement of breast tumor images

Abstract Breast cancer is the second largest disease in the world having high mortality rate among ladies. Breast image enhancement is required for the foundation of high accuracy segmentation. Several features are extracted from the ROI of mammogram in order to detect the cancer tumor in the early stage. Ultrasound breast images have the speckle noise during the capturing of image. In mammogram images also, the noises like Guassian, Normal and Poisson are introduced. There are no proper enhancements algorithms are designed to enhance the images for the preprocessing of the images. The image enhancement is broadly categorized into two different domains such as spatial and frequency. The spatial domain algorithms will smoothen the image quality and frequency domain algorithms will sharpen the image. Till today there is no concrete work on suitable algorithm methods using different domains of enhancement. The research work has been implemented in two stages. In the first stage we are identifying various noises in the mammogram and ultrasound images. In second stage the algorithms like mean, median, Homomorphic, Weiner, Modified Gabor, Anisoid diffusion filter, Contrast limited Adaptive Histogram equalization (CLAHE), Bilateral filter and Guided filter are worked out. Among all these filters we modified Gabor filter that clearly highlights the portion of the tumor. For all these filters Mean Square Error (MSE), RMSE, PSNR, VAR, SSIM, SD are computed. The best PSNR is obtained for the modified Gabor filter.
Solving the Travelling Salesman Problem Based on Collaborative Neurodynamic Optimization with Discrete Hopfield Networks

This paper addresses the travelling salesman problem (TSP) based on collaborative neurodynamic optimization (CNO). In the CNO approach to TSP, a population of discrete Hopfield networks are employed for searching local optimal solutions and repeatedly reinitialized by using the particle swarm optimization rule towards a global optimal solution. Experimental results for solving four TSP benchmarks are reported to substantiate the efficacy of the CNO approach.
Attention Driven Self-Similarity Capture for Motion Deblurring

Recently, deep learning-based algorithms have brought impressive results in deblurring tasks. However, as an image prior proved important in image restoration tasks, self-similarity was not exploited in motion deblurring. To tackle this problem, we propose an Attention Self-Similarity Capture (ASSC) module, which takes full advantage of self-similarity by capturing long-range feature dependencies. Besides, to achieve a trade-off between performance and efficiency, we design an Enhanced Spatial Attention (ESA) module, which can dynamically adapt to the spatially-varying motion blur. We employ patch-hierarchical architecture composed of the two modules mentioned above with parameter-free feature flow between different levels. Moreover, we build two large-scale datasets, GOPRO-Supplement and SONY-Extension, to expand the public GOPRO dataset’s scene and resolution. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on both the public GOPRO dataset and our datasets.
Automatic first-arrival picking through convolution kernel construction and particle swarm optimization

First-arrival picking is a necessary step in seismic data processing. Existing algorithms are either inaccurate or inefficient when the number of geophone groups is large and signal-to-noise ratio is low. In this paper, we propose the automatic first-arrival picking through convolution kernel construction and particle swarm optimization (FPCO) algorithm. First, abrupt energy fluctuations are detected using convolution kernels through convolution. Second, the locations of abrupt fluctuation are calculated in each convolution result to produce the index vectors. Third, the index vectors are integrated to one vector called the centerline by the Gaussian kernel. Finally, adaptive threshold is applied to obtain first arrivals around the centerline. The particle swarm optimization is applied to train the parameters. Experimental results on field datasets show that FPCO is more accurate and stable than popular algorithms.
Analysis of Duplicate Packing in FruitChain

Nakamoto's blockchain faces fairness and security barriers. Recently a famous blockchain protocol called FruitChain has been proposed to solve the two problems. In FruitChain, each miner performs hashing operations with a low difficulty to mine a fruit containing transactions. These fruits are then packed into blocks. The low difficulty implies that many miners might mine fruits in a short interval and the broadcasting nature of the blockchain network means that these miners might pack transactions from a similar transaction set into fruits. Therefore, in FruitChain, a transaction might be packed into multiple fruits. This paper is the first to identify the problem of duplicate packing in FruitChain. We then develop a theoretical model taking into account all kinds of factors (e.g., fruit generation rate, fruit size, network delay) to analyze the probability of duplicate packing of a transaction. Extensive simulations verify that our theoretical model is very accurate. This study is helpful for providing guidance for configuring FruitChain parameters.
Comparative study between KNN and maximum entropy classification in sentiment analysis of menstrual cup

Abstract The rise of reuse, reduce, recycle movement impacts both in pollutants management and waste. Including menstrual waste by using the menstrual cup. However, the use of menstrual cups in Indonesia is not yet popular. Sentiment analysis is needed to see how the public thinks about the menstrual cup. Using 1108 data from Twitter, which was then labeled into positive and negative manually, the sentiment analysis stage was carried out using Maximum Entropy and K-Nearest Neighbor. These two were chosen because maximum entropy works by obtaining the best probability distribution, which closest to reality, and k-nearest neighbors work by classifying new objects based on attribute examples and training data. The implementation program uses PyCharm IDE and python programming language. From the research results, maximum entropy and k-nearest neighbors accuracy each are 84.6% and 83.7% with 1108 tweets. In the next research, a lexicon dictionary can be used to replace the manual labeling process.
Enhanced Whale Optimization Algorithm for Large-Scale Global Optimization Problems

Whale optimization algorithm (WOA) is a relatively novel swarm-based optimization algorithm that has the advantage of less control parameters, strong global optimization ability and easy of implementation. However, there is still an insufficiency of low accuracy, premature, and slow convergence speed in tackling large-scale global optimization (LSGO) problems. To address the issue, an enhanced whale optimization algorithm (EWOA), which combines the original WOA with opposition-based learning, chaotic map and dynamic inertia weight, is proposed for solving LSGO problems. Chaotic opposition-based learning initialization strategy by comparing the fitness of an individual to its opposite and retaining well-diversified solutions is employed to accelerate convergence speed and improve the exploration. In addition, in order to make full use of and balance the exploration and exploitation of the original WOA, a novel chaotic cooperative updating strategy of nonlinear control parameter and dynamic inertia weight is given. Moreover, optimal individual chaotic searching strategy is integrated with evolutionary population updating to avoid the possibility of being trapped into local optima. The experimental results on four classical benchmark functions with dimensions ranging from 100 to 1000 demonstrate the effectiveness and efficiency of the chaotic opposition-based learning initialization strategy, chaotic nonlinear control parameter updating strategy and optimal individual chaotic searching strategy. The comparisons show that the proposed EWOA significantly improves the performance of WOA and provides competitive results for LSGO problems compared with state-of-the-art algorithms.
Steganography based Information Hiding and Transmission via SC-FDMA Transceiver

Hiding secret information is tedious in scientific world. Hacking data occurs in many places like colleges, banks etc. Due to this people were affected as they loss their data. The level of security should be high enough so hackers or third party cannot find or access the secret data. Various methods are used to stash the secret information. In this project SC-FDMA (Single Carrier-Frequency Division Multiple Access) has been designed to image transmission and to secure secret message steganography is used. The secret message is stashed in a binary image and the embedded binary image is embedded in a grey scale image using LSB based image steganography. The obtained image is transmitted using designed transceiver. Then extraction process is made in reverse order of embedding process. Then PSNR and MSE are used to analyze the standard of stego image. Histogram is used to know how much minute difference between original and stego image.
Spatially modulated thermal excitations for shearography non-destructive inspection of thick composites

With the increasing application of thick composites in marine, wind energy and aerospace industries, the inspection of thick composites becomes more and more challenging when considering the variety of thick structures (e.g., laminate, sandwich, honeycomb structures). Shearography is a full-field and non-contact optical non-destructive testing (NDT) method which is normally used to inspect composite laminates up to 10 mm while for the thick composite laminates (e.g., with the thickness of more than 50 mm), its performance is not clear yet. In shearography NDT, a defect-induced anomaly is revealed from fringe or phase maps obtained by comparing two states of deformation of the specimen to be inspected. Thermal loading is widely used to deform the specimen due to its advantages of convenience for on-site inspection and cost-effectiveness. The objective of this study is to improve the defect detection capabilities of shearography when used to inspect thick composites. For that, spatial modulated thermal excitations are investigated. A thick composite model has been built in Abaqus to assist the shearography inspection. Various kinds of spatially modulated heating including local heating and global heating are explored for thick composite inspection with shearography in order to evaluate the corresponding efficacies in defect detection. We will present both experimental and numerical results on spatial modulated thermal loading. Defect-induced shearographic responses subjected to local and global thermal excitations will be discussed in this paper, including the influence of short-time heating and long-time heating on thick composite inspection. Current results indicate that long-time heating is more favorable when inspecting deep defects in thick composites, and with local heating it is possible to increase the defect-induced signal when compared with global heating.
Graph-based saliency detection using a learning joint affinity matrix

The graph model is a reliable propagation mechanism in saliency detection, saliency value propagation diffusion results between any two nodes are determined merely by defining an effect affinity matrix. Most existing methods generally calculamatrix using mean values of single or multiple feature vectors, not fully exploit the diversity and consistency of multi-view features, may produce poor foreground uniformity and completeness in the complex scene. Multi-views should share an affinity matrix as well as complement each other. In this paper, we propose a graph-based saliency detection with a learning joint affinity matrix. First, we capture multiple appearance features from the image and generate a learning joint affinity matrix based on low-rank representation. Then, for computing an effect affinity matrix, we linearly integrate the traditional affinity matrix and learning joint affinity matrix, helping to construct an affinity graph for diffusion-based compactness. Finally, to effectively optimize the initial saliency map, we diffuse the learning joint affinity matrix and traditional impact factor matrix via cross-view diffusion processing, which begets an approving advantage for single-layer cellular automata. Results on three benchmark datasets demonstrate that our proposed method shows the best performance against nine state-of-the-art models.
An Importance of Crypto-Watermarking Techniques for Secure Transmission of Multimedia Information

Multimedia information is critical of examining, information perceived, and which are illustrated by the human cerebrum. A Crypto-watermarking approach reserves more popularity in certain important fields like medical, military, and law enforcement. This paper discusses detailed the importance of crypto-watermarking techniques and strategies used to improve information security. The main objectives of developing this crypto-watermarking application are that it can provide the user with the security of data. Also, these techniques aim to protect the Multimedia Contents aim to restrict the avoid unauthorized copies of digital documents. Payload and minimize the bit error rate are the parameters that are in line with these techniques.