Reviewerly/exHarmony huggingface dataset:
Contrastive time–frequency learning for radar signal sorting

With the development of radar technology, the electromagnetic environment becomes more and more complex. The acquisition of labeled data is costly, and the existing radar signal sorting and recognition methods have poor generalization capability. To address this problem, a radar signal recognition technique based on time–frequency analysis and contrastive learning is proposed in this paper. The classification process is improved according to the characteristics of radar signals without using labels. The proposed method, contrastive time–frequency learning, can use the unsupervised data to sort and identify radar signals. By augmenting the data with various channel conditions, the generalization ability of the classification model is also improved, especially for the situation with low SNR. The experiments prove the good performance of the proposed method, and classification accuracy for radar signals can be up to 98.17%.
Systems-of-Systems Design Patterns: A Systematic Literature Review and Synthesis

Design patterns are an established approach for reusing knowledge about good solutions to recurring problems. Patterns can be seen as a way of describing the best practices, and have been used in many different fields, ranging from building architecture and city planning to software development. There are also scattered results relating to patterns for systems-of-systems. The purpose of this paper is to summarize and review the literature on patterns for systems-of-systems and make a synthesis of a recommended approach for the field. Specifically, the novel contributions of the paper are to propose a consolidated structure for describing individual patterns and suggest the dimensions along which a pattern catalog can be organized. The paper also summarizes the concrete patterns suggested in the existing literature and classifies them according to the recommended structure.
Multi-label chain clustering-classification and regression predictive models for patient punctuality and turnaround time in outpatient primary care settings

This study develops two multi-label chain machine learning predictive models to anticipate patient punctuality and turnaround time. The first model uses an integrated model of clustering and classification where the check-in, service, and checkout times are clustered into three categories using the K-means algorithm. Then, patient punctuality and established clusters are used to develop a multi-label chain predictive model that utilizes Logistic Regression, Multi-Layer Perceptron, and tree-based classifiers. The second model predicts patient punctuality and turnaround time using a multi-label chain regression model that utilizes Linear Regression, Huber Regressor, ADR Regression, Multi-Layer Perceptron, and tree-based regressors. It was found that a patient’s age is a key driver for both patient punctuality and turnaround time. Also, there is a significant association between patient punctuality and turnaround time. The first proposed model predicted the punctuality and turnaround time with an average best F1-score of about 70.4% and 71.9%, respectively. The second model produced acceptable results with the best average R-squared of 0.67 for punctuality and 0.68 for turnaround time. The models can reduce the complexity and time of predicting several numerical outputs, enhance the interpretation of the results, and improve the understanding of the results by non-technical staff.
Pre-Training and Fine-Tuning with Next Sentence Prediction for Multimodal Entity Linking

As an emerging research field, more and more researchers are turning their attention to multimodal entity linking (MEL). However, previous works always focus on obtaining joint representations of mentions and entities and then determining the relationship between mentions and entities by these representations. This means that their models are often very complex and will result in ignoring the relationship between different modal information from different corpus. To solve the above problems, we proposed a paradigm of pre-training and fine-tuning for MEL. We designed three different categories of NSP tasks for pre-training, i.e., mixed-modal, text-only and multimodal and doubled the amount of data for pre-training by swapping the roles of sentences in NSP. Our experimental results show that our model outperforms other baseline models and our pre-training strategies all contribute to the improvement of the results. In addition, our pre-training gives the final model a strong generalization capability that performs well even on smaller amounts of data.
Further Enhancement of KNN Algorithm Based on Clustering Applied to IT Support Ticket Routing

Companies receive millions of tickets from their clients. Unfortunately, manual ticket routing takes time and relies heavily on human resources. To help automate the ticket routing, text classification can assist as it is the process of categorizing a document into a predetermined class based on its content. One algorithm is the K-Nearest Neighbors (KNN) which is a popular supervised technique but ranks average to lowest compared to other classification models. An improved KNN algorithm utilized clustering and improved the accuracy of the classifier. This paper proposed a further enhancement of this algorithm by adding preprocessing techniques, changing the distance formula, and computing for the k-value rather than choosing one. Two datasets of IT support tickets were used to train and test the algorithms. Results showed that this further enhanced algorithm significantly performed better than the initial algorithm with the highest accuracy score of 97.83% in one dataset while the initial algorithm performed best with an accuracy score of 86.34% using a k-value of 4 in another dataset.
Research on Financial Development of Water Resources Enterprises Based on Blockchain Technology

From the perspective of economics, blockchain is simply an Internet, which can meet the needs of the sharing economy to a certain extent. With the development of the world, blockchain technology has developed in an explosive growth. At present, the systematic research on blockchain technology at home and abroad is still in its initial stage, mainly focusing on the application prospect, especially in the fields of traditional finance and digital currency. The development of environmentally friendly water resources enterprises has the support background of the country, and the financial impact of blockchain technology also affects environmentally friendly water resources enterprises. In order to explore the development of blockchain technology in water resources protection, based on the advantages and application status of blockchain technology, this paper firstly constructs a supply chain financial system model of water resources enterprises based on blockchain and secondly analyzes the financial environment of seven water enterprises in China by constructing a financial environment evaluation model. The results show that Chongqing Water has the highest target degree of 0.76 and target level of 3, indicating that the enterprise has good adaptability and maintains the stability of financial environment under the influence of blockchain technology. Furthermore, the asset valuation of Chongqing Water is analyzed by Monte Carlo method. The results show that, under the blockchain financial model, the fierce competition in the wealth management products market will make issuers lose their dominant position in pricing and reduce profits in order to attract investors.
IoT Security for Smart Home: Issues and Solutions

The Internet of Things or IoTs are becoming popular for their ability to be embedded in various useful devices, like sensors. As a result, they are also becoming part of our domestic technology. The devices at our home that can be connected to internet are no longer limited to Televisions but mirrors, doorbells, vacuum cleaners, kitchen appliances etc. and these smart devices constitute a smart home. When the domestic devices or smart home components connect to the internet, they become subjects to cyber-attacks. This paper presents a survey of current viable methods as a measure against such security issues. Automated firmware analysis, machine learning, and blockchain technology are all vastly different approaches that focus on various aspects of security. Just as the innovation in smart devices persists, these solutions will evolve to protect the IoT devices of the future.
Bitcoin: Cryptographic Algorithms, Security Vulnerabilities and Mitigations

Blockchain technology has made it possible to store and send digital currencies. Bitcoin wallets and marketplaces have made it easy for nontechnical users to use the protocol. Since its inception, the price of Bitcoin is going up and the number of nodes in the network has increased drastically. The increasing popularity of Bitcoin has made exchanges and individual nodes a target for an attack. Understanding the Bitcoin protocol better helps security engineers to harden the network and helps regular users secure their hot wallets. In this paper, Bitcoin protocol is presented with description of the mining process which secures transactions. In addition, the Bitcoin algorithms and their security are described with potential vulnerabilities in the protocol and potential exploits for attackers. Finally, we propose some security solutions to help mitigate attacks on Bitcoin exchanges and hot wallets.
LADDER: Latent boundary-guided adversarial training

Abstract Deep Neural Networks (DNNs) have recently achieved great success in many classification tasks. Unfortunately, they are vulnerable to adversarial attacks that generate adversarial examples with a small perturbation to fool DNN models, especially in model sharing scenarios. Adversarial training is proved to be the most effective strategy that injects adversarial examples into model training to improve the robustness of DNN models against adversarial attacks. However, adversarial training based on the existing adversarial examples fails to generalize well to standard, unperturbed test data. To achieve a better trade-off between standard accuracy and adversarial robustness, we propose a novel adversarial training framework called LAtent bounDary-guided aDvErsarial tRaining (LADDER) that adversarially trains DNN models on latent boundary-guided adversarial examples. As opposed to most of the existing methods that generate adversarial examples in the input space, LADDER generates a myriad of high-quality adversarial examples through adding perturbations to latent features. The perturbations are made along the normal of the decision boundary constructed by an SVM with an attention mechanism. We analyze the merits of our generated boundary-guided adversarial examples from a boundary field perspective and visualization view. Extensive experiments and detailed analysis on MNIST, SVHN, CelebA, and CIFAR-10 validate the effectiveness of LADDER in achieving a better trade-off between standard accuracy and adversarial robustness as compared with vanilla DNNs and competitive baselines.
An innovative hybrid algorithm for solving combined economic and emission dispatch problems

As environmental concerns have grown, the combined economic emission dispatch (CEED) problem has gotten a lot of attention. Both the cost of fuel and the emission pollution caused by it must be kept to a minimum. As a result, this paper presents an innovative hybrid approach (ihPSODE) for solving CEED problems. This hybrid technique incorporated novel differential evolution (nDE) and particle swarm optimization (nPSO). Where nDE introduces an improved mutation and crossover approach (to prevent untimely convergence) as well as nPSO introduces a new acceleration coefficient, inertia weight and position improve equation (to alleviate the stagnation). So as to balance among local and global search ability, after ihPSODE population evaluation, the best half individuals are determined and the rest individuals are discarded. Then, nPSO is used in the current population (to sustain exploration and exploitation) and nDE is employed in the nPSO generated population (to improve convergence accuracy). The competence of the proposed algorithms (ihPSODE, nPSO and nDE) is inspected on 23 unconstrained benchmark function and then solved 3 test system (3-, 6- and 40-unit) of economic load dispatch (ELD) and 3 test system (3-, 10- and 40-unit) of CEED problem. The experiments have denoted that the proposed algorithms show competitive results and significant performances.
Research and Application of Deformation Prediction Model for Deep Foundation Pit Based on LSTM

Deep foundation pit is a door with a long history, but it has new disciplines; in this paper, firstly, the modeling method and process of LSTM (long short-term memory) network are discussed in detail, then the optimization algorithm used in the model is described in detail, and the parameter selection methods such as initial learning rate, activation function, and iteration number related to LSTM network training are introduced in detail. LSTM network is used to process the deformation data of deep foundation pit, and random gradient descent, momentum, Nesterov, RMSProp, AdaGmd, and Adam algorithms are selected in the same example for modeling prediction and comparison. Two examples of horizontal displacement prediction of pile and vertical displacement prediction of column in deep foundation pit show that the LSTM network model established by different optimization algorithms has different prediction accuracy, and the LSTM network model established by Adam optimization algorithm has the highest accuracy, which proves that the selection of optimization algorithm plays an important role in LSTM and also verifies the feasibility of LSTM network in the data processing and prediction of deep foundation pit deformation.
Learning agents that acquire representations of social groups

Humans are learning agents that acquire social group representations from experience. Here, we discuss how to construct artificial agents capable of this feat. One approach, based on deep reinforcement learning, allows the necessary representations to self-organize. This minimizes the need for hand-engineering, improving robustness and scalability. It also enables "virtual neuroscience" research on the learned representations.
One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education

In this paper, one multidisciplinary-applicable aggregated model has been proposed and verified. This model uses traditional techniques, on the one hand, and algorithms of machine learning as modern techniques, on the other hand, throughout the determination process of the relevance of model attributes for solving any problems of multicriteria decision. The main goal of this model is to take advantage of both approaches and lead to better results than when the techniques are used alone. In addition, the proposed model uses feature selection methodology to reduce the number of attributes, thus increasing the accuracy of the model. We have used the traditional method of regression analysis combined with the well-known mathematical method Analytic Hierarchy Process (AHP). This approach has been combined with the application of the ReliefF classificatory modern ranking method of machine learning. Last but not least, the decision tree classifier J48 has been used for aggregation purposes. Information on grades of the first-year graduate students at the Criminalistics and Police University, Belgrade, after they chose and finished one of the three possible study modules, was used for the evaluation of the proposed model. To the best knowledge of the authors, this work is the first work when considering mining closed frequent trees in case of the streaming of time-varying data.
<scp>i</scp> <scp>S</scp> <scp>pan</scp> : Parallel Identification of Strongly Connected Components with Spanning Trees

Detecting strongly connected components (SCCs) in a directed graph is crucial for understanding the structure of graphs. Most real-world graphs have one large SCC that contains the majority of the vertices as well as many small SCCs whose sizes are reversely proportional to the frequency of their occurrences. For both types of SCCs, current approaches that rely on depth first search (DFS) or breadth first search (BFS) face the challenges of both strict synchronization requirements and high computation cost. In this article, we advocate a new paradigm of identifying SCCs with simple spanning trees since SCC detection requires only the knowledge of connectivity among the vertices. We have developed a prototype called i S pan , which consists of parallel, relaxed synchronization construction of spanning trees for detecting large and small SCCs combined with fast trims for small SCCs. We further scale i S pan to the distributed memory system by applying different distribution strategies to the data and task parallel jobs. Not limited, we also extend i S pan to the GPU architecture. The evaluations show that i S pan is able to significantly outperform current state-of-the-art DFS- and BFS-based methods by an average 18× and 4×, respectively.
Exploring System and Machine Learning Performance Interactions when Tuning Distributed Data Stream Applications

Deploying machine learning (ML) applications over distributed stream processing engines (DSPEs) such as Apache Spark Streaming is a complex procedure that requires extensive tuning along two dimensions. First, DSPEs have a vast array of system configuration parameters (such as degree of parallelism, memory buffer sizes, etc.) that need to be optimized to achieve the desired levels of latency and/or throughput. Second, each ML model has its own set of hyper-parameters that need to be tuned as they significantly impact the overall prediction accuracy of the trained model. These two forms of tuning have been studied extensively in the literature but only in isolation from each other. This position paper identifies the necessity for a combined system and ML model tuning approach based on a thorough experimental study. In particular, experimental results have revealed unexpected and complex interactions between the choices of system configuration and hyper-parameters, and their impact on both application and model performance. These findings open up new research directions in the field of self-managing stream processing systems.
Research on Multichannel 3D Oil Painting Style Rendering Model Based on 3DS Max

People are becoming less comfortable in content as a form of data throughout this period of growing scientific and technological advancements. Oil painting also was challenged in unprecedented ways by modern image processing techniques as person’s primary source of data, and it had a tremendous effect on the domain of artistic creativity. Therefore, we present an upgraded convolutional neural network (U-CNN) for the enhancement of style rendering model addressing the aforementioned challenges. Initially, the datasets are gathered that are denoised and enhanced in the preprocessing stage to eliminate noise and improve the quality of the data. Gabor filter bank (GFB) is employed in the feature extraction stage to extract several features from the normalized data. For the application of style rendering model, the proposed approach is utilized. Moreover, through applying the 3DS MAX model, the three-dimensional (3-D) oil painting is generated. Finally, the performance of the proposed approach is examined and compared with other existing approaches to obtain the proposed approach with the greatest effectiveness. The findings are depicted in chart form by employing the origin tool.
Turkish Data-to-Text Generation Using Sequence-to-Sequence Neural Networks

End-to-end data-driven approaches lead to rapid development of language generation and dialogue systems. Despite the need for large amounts of well-organized data, these approaches jointly learn multiple components of the traditional generation pipeline without requiring costly human intervention. End-to-end approaches also enable the use of loosely aligned parallel datasets in system development by relaxing the degree of semantic correspondences between training data representations and text spans. However, their potential in Turkish language generation has not yet been fully exploited. In this work, we apply sequence-to-sequence (Seq2Seq) neural models to Turkish data-to-text generation where the input data given in the form of a meaning representation is verbalized. We explore encoder-decoder architectures with attention mechanism in unidirectional, bidirectional, and stacked recurrent neural network (RNN) models. Our models generate one-sentence biographies and dining venue descriptions using a crowdsourced dataset where all field value pairs that appear in meaning representations are fully captured in reference sentences. To support this work, we also explore the performances of our models on a more challenging dataset, where the content of a meaning representation is too large to fit into a single sentence, and hence content selection and surface realization need to be learned jointly. This dataset is retrieved by coupling introductory sentences of person-related Turkish Wikipedia articles with their contained infobox tables. Our empirical experiments on both datasets demonstrate that Seq2Seq models are capable of generating coherent and fluent biographies and venue descriptions from field value pairs. We argue that the wealth of knowledge residing in our datasets and the insights obtained from this study hold the potential to give rise to the development of new end-to-end generation approaches for Turkish and other morphologically rich languages.
Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space

This paper addresses an important problem of ranking the pre-trained deep neural networks and screening the most transferable ones for downstream tasks. It is challenging because the ground-truth model ranking for each task can only be generated by fine-tuning the pre-trained models on the target dataset, which is brute-force and computationally expensive. Recent advanced methods proposed several lightweight transferability metrics to predict the fine-tuning results. However, these approaches only capture static representations but neglect the fine-tuning dynamics. To this end, this paper proposes a new transferability metric, called Self-challenging Fisher Discriminant Analysis (SFDA), which has many appealing benefits that existing works do not have. First, SFDA can embed the static features into a Fisher space and refine them for better separability between classes. Second, SFDA uses a self-challenging mechanism to encourage different pre-trained models to differentiate on hard examples. Third, SFDA can easily select multiple pre-trained models for the model ensemble. Extensive experiments on 33 pre-trained models of 11 downstream tasks show that SFDA is efficient, effective, and robust when measuring the transferability of pre-trained models. For instance, compared with the state-of-the-art method NLEEP, SFDA demonstrates an average of 59.1% gain while bringing 22.5x speedup in wall-clock time. The code will be available at https://github.com/TencentARC/SFDA .