Reviewerly/exHarmony huggingface dataset:
Model optimization of English intelligent translation based on outlier detection and machine learning

With the actual demand of international communication, intelligent translation of English has become a key direction of artificial intelligence development in the current English field. For the existing English intelligent translation, how to deal with the massive data effectively is always a big problem. Therefore, it is necessary to use the principle of machine learning to optimize the English intelligent translation model. The purpose of this paper is to optimize the existing English intelligent translation model through spectral clustering to remove outliers, so as to make it more suitable for the use of massive data. Moreover, this paper uses deep learning methods to improve on the basis of the PoseNet network structure and adds regularization to the convolutional layer, which reduces the problem of gradient disappearance and reduces the computational complexity. In addition, this paper uses adaptive weighting to remove invalid model assumptions. In the conceptual space of the similarity matrix, the interior point is farther from the origin than the outlier point. The algorithm in this paper can detect and eliminate the outliers in a paragraph of English. At the same time, the normal English content will be classified into data categories, and the final translation result will be obtained. Through the experimental test, it can be seen that the model proposed in this paper has good performance and can cope with massive data, so it has certain superiority.
Internet financial forecasting and digital economy development by using machine learning algorithm in the new consumption environment

Data plane development kit (DPDK) provides an effective solution for the rapid processing of general multi-core processor platforms. Research based on DPDK on a general-purpose multi-core processor platform can use CPU cache to improve packet forwarding and processor core performance, as well as network function virtualization performance. In the parallel development of finance and network, people find that complex financial problems can be well-solved through big data network. The rapidly changing financial market is affected by economic and political factors, and the generation of financial data is also highly uncertain. However, financial forecast analysis and forecast modeling are of great significance. As a whole, this trend could help inform policy; from a meso-level, it can be insightful. The changes in financial data are conducive to the planning of business strategies for enterprises and gaining market competitive advantages; from a micro-level, individual investors will pay attention to historical data changes and the development of industries or companies before making investment decisions in order to obtain benefits. Internet technology can strengthen the accuracy of financial forecasting, so as to effectively avoid risks, but also can promote the development of digital economy, reduce labor costs, improve the breadth and depth of financial services in digital economy.
MSCDP: Multi-step crowd density predictor in indoor environment

Monitoring and predicting crowd movements in indoor environments are of great importance in crowd management to prevent crushing and trampling. Existing works mostly focused on individual trajectory forecasting in a less crowded scene, or crowd counting and density estimation. Only a very few works predict the crowd density distribution. However, this study is failing to realize multi-step prediction or exploits only density heatmaps modality and ignores the information complementation with corresponding video frames. Therefore, we are motivated to predict crowd density distribution in multiple time steps to facilitate long-term prediction. In this paper, a Multi-Step Crowd Density Predictor (MSCDP) to fuse video frame sequences and corresponding density heatmaps, is proposed to accurately forecast future crowd density heatmaps. To capture long-term periodic movement features, the long-term optical flow context memory (LOFCM) module is designed to store learnable patterns. We conducted extensive experiments on two real-world datasets. Evaluation results show that our MSCDP outperforms the state-of-the-art baseline techniques and MSCDP variants in terms of various prediction errors, demonstrating the effectiveness of MSCDP and each of its key components in multi-step crowd density prediction.
An undersampling phase measurement algorithm based on parameter estimation for homodyne interferometer

To meet the needs of high speed measurement on hardware, undersampling technique is applied to displacement measurement homodyne interferometer. For orthogonal undersampled signals, the phase to be measured cannot be solved by arctangent function and original unwrapping algorithm. In this paper, a corresponding algorithm is proposed to measure the phases of the undersampled signals in practical conditions. The algorithm is a combination of the original phase solving algorithm and the compensation period. In the algorithm, the phase estimation and the error parameter model based on least square fitting are used to iteratively solve the desired phase. The proposed method is verified by simulations and experiments, and results demonstrate the high feasibility and accuracy of the algorithm.
Stochastic versus dynamic resonant activation to enhance threshold detector sensitivity

The Josephson junction, as a threshold detector, is studied in the presence of noise and a weak external signal in the framework of the pendulum model. A range of parameters, efficient for weak signal detection has been found. While in the low noise limit the signal can be enhanced via dynamic resonant activation mechanism, even weaker signals can be efficiently detected with the help of noise via stochastic resonant activation mechanism. The parameter optimizations of a microwave single photon counter prototype based on aluminum Josephson junction are discussed.
Design of 3D animation color rendering system supported by cloud computing based on genetic algorithm

The wireless network is composed of multiple micronetwork nodes that are difficult to replace. These network nodes are usually located in areas with poor environmental conditions, so the energy consumption and security of nodes have become the main limitations and challenges. Therefore, this paper discusses the network topology, data transmission and node trust mechanism, and proposes a cloud computing technology algorithm for intelligent regional wireless network. According to the data of arrival delay difference in intelligent regional wireless network environment, the approximate location of mobile system is set as different positioning results. The dynamic position of the mobile system is obtained through multiple calculations. With the support of cloud computing technology, this paper also studies the color rendering system of 3D animation. 3D animation can not only express emotions and feelings, but also color render through the facial expressions of characters in the animation, so as to enhance the entertainment and interest visible to the audience. However, due to the great demand for computing resources and color rendering resources, at present, the main research and implementation are carried out on the desktop platform. There are also solutions to use high-end infrared devices for 3D animation color rendering on the mobile system. However, because this method has high cost in use and does not have universality on the mobile device, this paper proposes a rendering system suitable for the general mobile platform. This paper studies the 3D animation color rendering system based on wireless network cloud computing technology, hoping to realize the universality of the system with the support of technology.
SiamRank: A siamese based visual tracking network with ranking strategy

Visual tracking is one of the most fundamental and active research topics in the field of computer vision with industrial applications. It has to solve 2 core problems, namely classification and state estimation. Most of the existing trackers utilize deep networks to extract the features of the object. Especially, Siamese based approaches have prevailed in tracking tasks, which generate labels for both positive and negative samples. However, these approaches introduce ambiguities and inaccurate semantic information at the same time, which may cause failure in classification. To address this problem, we present SiamRank by adding the sequential information of different samples in one image. We apply the proposed network to 2 backbones (AlexNet and GoogLeNet) to testify its general performance. Extensive experiments have been carried out on 7 popular benchmarks, including OTB100, LaSOT, GOT-10 K, TrackingNet, NFS, UAV123 and VOT2019, and our tracker achieves state-of-the-art results. Specifically, on both large-scale TrackingNet dataset and long-time LaSOT dataset, SiamRank surpasses the previous approaches with a relative gain of 10%, while running at 65 FPS.
Augmented reality experience in an architectural design studio

Thanks to the developing technology, different methods and tools are used in architectural representation, and architects contribute to developing these tools. Architects can easily model their designs with computer technologies and even make them visible in the environment with augmented reality technologies. Also, it is thought that these technologies will become widespread in basic architectural education over time. This study conducted a practice at the undergraduate architecture level using augmented reality technology. Within the scope of the study, the predisposition of students who experience augmented reality technologies to new representation, gender, course period, familiarity with playing computer games, the effect of computer programs that they can use in their previous augmented reality experience and designs if any, and the correlation between them has been tried to be revealed with this study. In this context, a study was carried out with thirteen students in the architectural design studio. Students were expected to parametrically design indoor exhibition elements using Rhinoceros 3D software and the Grasshopper plugin in the study. Then, an experimental augmented reality study was conducted with the Fologram, in which the students transferred these virtual exhibition elements they designed to the real indoors and reshaped them according to the space. Afterwards, a questionnaire consisting of five independent and ten dependent variables was applied to the students. The survey results were analysed with the IBM SPSS Statistics program. According to the results obtained, significant results were determined between 4 independent variables and one dependent variable.
Homotopy Continuation Method for Discounted Zero-Sum Stochastic Game with ARAT Structure

In this paper, we introduce a new function to trace the trajectory by applying the modified homotopy continuation method for finding the solution of two person zero-sum discounted stochastic ARAT game. For the proposed algorithm, the homotopy path approaching the solution is smooth and bounded.
Evolving a Pipeline Approach for Abstract Meaning Representation Parsing Towards Dynamic Neural Networks

Meaning Representation parsing aims to represent a sentence as a structured, Directed, Acyclic Graph (DAG), in an attempt to extract meaning from text. This paper extends an existing 2-stage pipeline AMR parser with state-of-the-art techniques in dependency parsing. First, Pointer-Generator Networks are used for out-of-vocabulary words in the concept identification stage, with an improved initialization via the use of word-and character-level embeddings. Second, the performance of the Relation Identification module is improved by jointly training the Heads Selection and the Arcs Labeling components. Last, we underline the difficulty of end-to-end training with recurrent modules in a static deep neural network construction approach and explore a dynamic construction implementation, which continuously adapts the computation graph, thus potentially enabling end-to-end training in the proposed pipeline solution.
On The Fairness of Multitask Representation Learning

In the context of multitask learning (MTL), representation learning is often accomplished through a feature-extractor ϕ that is shared across all tasks. This way, intuitively, the statistical cost of learning ϕ is collaboratively split across all tasks which enables sample efficiency. In this work, we consider a novel fairness scenario where T tasks can be split into majority and minority groups of sizes T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maj</inf> and T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</inf> respectively: The group assignments are unknown during MTL and T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</inf> /T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maj</inf> ratio corresponds to the imbalance level of the problem. We further assume that these groups admit r <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> , r <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -dimensional linear representations which are orthogonal to each other, thus, they would not benefit each other during MTL. Our main finding is that misspecification disproportionately hurts the minority tasks and over-parameterization is key to ensuring fairness of MTL representations. Specifically, we prove that, when we fit a R = r <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> dimensional misspecified representation, MTL model achieves small task-averaged risk however it has vanishing explanatory power on minority tasks. Conversely, when we fit a R = r <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> + r <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> dimensional well-specified representation, MTL model achieves small risks on both majority and minority tasks which are on par with the oracle baseline of training each group individually with the hindsight knowledge of assignments. Finally, we provide experimental results which are consistent with our theoretical findings.
Interpretable Data Mining Approaches to Predict Term Deposits Subscriptions

Under the covid pandemic, informatization has accelerated. Customer behaviors have changed dramatically in almost every industry, which requires companies and organizations to analyze the latest customer features and develop specialized marketing contents. Banks play a major role in the financial markets, and term deposit subscription is one of the most important products of banks. In this study, a Portuguese retail bank’s telemarketing data on selling term deposits has been analyzed which includes 16 input variables. All 16 variables have been converted to numeric inputs by using one-hot encoding method. For testing, the dataset has been divided into train and test groups by 80% and 20% respectively. To make this study more interpretable, two tree-structured data mining models, decision tree and random forest classifier, has been trained and tested. The input variable called “Duration” can only be known after the outcome is known so it has been dropped when creating predictive models. Two feature importance histograms and three confusion matrics are generated to visualize the customer features and evaluate the models. Area under Curve-Receiver Operating Characteristic Score (AUC-ROC scores) are also computed to compare the accuracy of models. Testing results have proved that “duration” has a severe relationship with clients’ subscriptions and should not be included in predictive models. Random forest classifier is better than decision tree and can generate comparable accuracy to previous models which included “duration.” Balance and age are the top two influential customer features on term deposit subscriptions.
Research on the Application of Blockchain Technology in the Financial Field

Decentralization, autonomy, integrity, immutability, verification, fault-tolerance, anonymity, auditability, and transparency are desirable characteristics of blockchain technology. Blockchain technology originated from Bitcoin which is one of the applications of digital currency. From a financial point of view, it is a distributed data storage system. Therefore, it is very important to analyze the current research status in this field, and further sort out the deficiencies of current research and future development direction. This article describes the definition of blockchain technology and hash algorithm first. Besides, it also shows some applications including supply chain finance, digital ticket, and digital currency in the financial field. Privacy and risk are the key points to worry about. This article also introduces how to protect user privacy in supply chain finance. This article analyzes how blockchain effectively protects user privacy from banks and non-banks. It is difficult to take into account all three features of blockchain: security, scalability, and decentralization. However, the integration of finance and technology is an increasingly strong trend.
A Pair-Task Heuristic for Scheduling Tasks in Heterogeneous Multi-cloud Environment

Heterogeneous multi-cloud environments make use of a collection of diverse performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications in the multi-cloud environment require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Scheduling tasks to distributed heterogeneous clouds is termed NP-complete which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of ‘n’ tasks in two groups among a set of 'm' clouds, three heuristicsPair-Task Threshold Limit (PTL), PTMax-Min, and PTMin-Max are proposed. Firstly, proposedheuristics calculate tasks threshold valuebased on the tasks attributes to determine the tasks scheduling order and then tasks are sorted in descending order of threshold value. Group 1 comprises ([n/2]) tasks ordered in descending value of threshold. Group 2 comprises remaining tasks ([n/2] − 1) ordered in ascending value of threshold. Secondly, tasks form group 1 are scheduled first based on minimum completion time, and then tasks in group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. The proposed approaches PTL, PTMax-Min, and PTMin-Max explicitly shows the better results in terms of reduced makespan, completion time, response time and more resource utilization compared to MCT, MET, and Min-min.
Efficient thermal infrared tracking with cross-modal compress distillation

The key issue of thermal infrared tracking is to use neural networks to represent the target effectively and efficiently in the thermal infrared domain. The lack of thermal infrared trainable datasets makes it difficult to train a robust infrared object tracker from scratch, and the time-consuming convolution operations also make the tracking slow. To address the above problems, we proposed cross-modal compression distillation to represent thermal infrared objects for tracking, by leveraging an off-the-shelf RGB model with knowledge distillation. Specifically, cross-modal distillation is performed to effectively transfer knowledge from RGB modality to thermal infrared modality by inputting paired RGB and thermal infrared images into two branches of a Siamese network. Additionally, based on the teacher–student model architecture, the feature extractor is compressed into a lightweight model by model pruning and multi-level deep feature matching. Experimental results on LSOTB-TIR and PTB-TIR datasets show that the thermal infrared object tracking models distilled by our proposed method achieved faster tracking speed with better performance than the baseline RGB tracker by gaining an improvement of 1.5% Success Rate, 2.2% Precision, and 1.9% Normalized Precision, 58 frames per second (FPS) on LSOTB-TIR dataset, respectively.
Towards liver segmentation in the wild via contrastive distillation

Automatic liver segmentation is a key component for performing computer-assisted hepatic procedures. The task is challenging due to the high variability in organ appearance, numerous imaging modalities, and limited availability of labels. Moreover, strong generalization performance is required in real-world scenarios. However, existing supervised methods cannot be applied to data not seen during training (i.e. in the wild) because they generalize poorly.We propose to distill knowledge from a powerful model with our novel contrastive distillation scheme. We use a pre-trained large neural network to train our smaller model. A key novelty is to map neighboring slices close together in the latent representation, while mapping distant slices far away. Then, we use ground-truth labels to learn a U-Net style upsampling path and recover the segmentation map.The pipeline is proven to be robust enough to perform state-of-the-art inference on target unseen domains. We carried out an extensive experimental validation using six common abdominal datasets, covering multiple modalities, as well as 18 patient datasets from the Innsbruck University Hospital. A sub-second inference time and a data-efficient training pipeline make it possible to scale our method to real-world conditions.We propose a novel contrastive distillation scheme for automatic liver segmentation. A limited set of assumptions and superior performance to state-of-the-art techniques make our method a candidate for application to real-world scenarios.
An improved golden jackal optimization for multilevel thresholding image segmentation

Aerial photography is a long-range, non-contact method of target detection technology that enables qualitative or quantitative analysis of the target. However, aerial photography images generally have certain chromatic aberration and color distortion. Therefore, effective segmentation of aerial images can further enhance the feature information and reduce the computational difficulty for subsequent image processing. In this paper, we propose an improved version of Golden Jackal Optimization, which is dubbed Helper Mechanism Based Golden Jackal Optimization (HGJO), to apply multilevel threshold segmentation to aerial images. The proposed method uses opposition-based learning to boost population diversity. And a new approach to calculate the prey escape energy is proposed to improve the convergence speed of the algorithm. In addition, the Cauchy distribution is introduced to adjust the original update scheme to enhance the exploration capability of the algorithm. Finally, a novel "helper mechanism" is designed to improve the performance for escape the local optima. To demonstrate the effectiveness of the proposed algorithm, we use the CEC2022 benchmark function test suite to perform comparison experiments. the HGJO is compared with the original GJO and five classical meta-heuristics. The experimental results show that HGJO is able to achieve competitive results in the benchmark test set. Finally, all of the algorithms are applied to the experiments of variable threshold segmentation of aerial images, and the results show that the aerial photography images segmented by HGJO beat the others. Noteworthy, the source code of HGJO is publicly available at https://github.com/Vang-z/HGJO.