Reviewerly/exHarmony huggingface dataset:
Exploiting semantic segmentation to boost reinforcement learning in video game environments

Abstract In this work we explore enhancing performance of reinforcement learning algorithms in video game environments by feeding it better, more relevant data. For this purpose, we use semantic segmentation to transform the images that would be used as input for the reinforcement learning algorithm from their original domain to a simplified semantic domain with just silhouettes and class labels instead of textures and colors, and then we train the reinforcement learning algorithm with these simplified images. We have conducted different experiments to study multiple aspects: feasibility of our proposal, and potential benefits to model generalization and transfer learning. Experiments have been performed with the Super Mario Bros video game as the testing environment. Our results show multiple advantages for this method. First, it proves that using semantic segmentation enables reaching higher performance than the baseline reinforcement learning algorithm without modifying the actual algorithm, and in fewer episodes; second, it shows noticeable performance improvements when training on multiple levels at the same time; and finally, it allows to apply transfer learning for models trained on visually different environments. We conclude that using semantic segmentation can certainly help reinforcement learning algorithms that work with visual data, by refining it. Our results also suggest that other computer vision techniques may also be beneficial for data prepossessing. Models and code will be available on github upon acceptance.
Bitcoin or Gold?

In the topic selection, we need to estimate the prices of bitcoin and gold according to the data given from 2001 to 2012. According to the estimated price, the initial amount is set as $1000, which is used as the principal for financial investment for a period of five years from 2016. In the whole modeling process, the main problems we need to solve are the following four points: the task 1 is the best investment strategy is given through the established model, and the investment value on October 9, 2021 is calculated. The task 2 is the best strategy of the model is proved. The task 3 is Determine the impact of transaction costs on transaction results. The last task is to Complete a memo with strategies, models, and results. In the whole modeling process, we first preprocess the data, which is arranged and classified in chronological order, and fill the data by interpolation fitting. LSMT algorithm is a neural network algorithm, which is suitable for the calculation of various long-term processes. The investment problem we study is a good application field. In the calculation process of the basic model, it is necessary to set the initial value, complete a series of processing, and process the hidden layer of LSTM unit. Take x as the output value, set the temporary hidden layer and new hidden layer, and verify that the size of the final output result is consistent with the label size. The hidden layer is transformed according to the sigmoid function proposed above. After calculating the hidden layer conversion, the error is back propagated, the input derivative of the file is obtained, and the overall error and record hidden layer are obtained. Then the calculated hidden layer difference is used to calculate the change of parameters and update parameters. Since bitcoin can be traded on any trading day, gold can only be traded on weekly trading days. For the convenience of calculation, we fix the transactions of bitcoin and gold as trading days every Friday. After receiving the benefits, the total assets of the cash flow as of the trading day are obtained by deducting the Commission to be paid. However, the model ignores the impact of bitcoin mining with different software and the fact that gold and bitcoin are not fixed on the same trading day, so there will be errors.
Example Driven Code Review Explanation

Background: Code reviewing is an essential part of software development to ensure software quality. However, the abundance of review tasks and the intensity of the workload for reviewers negatively impact the quality of the reviews. The short review text is often unactionable. Aims: We propose the Example Driven Review Explanation (EDRE) method to facilitate the code review process by adding additional explanations through examples. EDRE recommends similar code reviews as examples to further explain a review and help a developer to understand the received reviews with less communication overhead. Method: Through an empirical study in an industrial setting and by analyzing 3,722 Code reviews across three open-source projects, we compared five methods of data retrieval, text classification, and text recommendation. Results: EDRE using TF-IDF word embedding along with an SVM classifier can provide practical examples for each code review with 92% F-score and 90% Accuracy. Conclusions: The example-based explanation is an established method for assisting experts in explaining decisions. EDRE can accurately provide a set of context-specific examples to facilitate the code review process in software teams.
Today’s library psychology in the context of N.A. Rubakin’s legacy

An outstanding Russian enlightener N.A. Rubakin left rich scholarly legacy related to the study of psychological and pedagogical aspects of reading. The article deals with the basic theoretical provisions of library psychology, as well as shows the relevance of this field of scholarship and practice for the libraries of today. The author outlines the future of the development of library psychology and library pedagogy in the reality of the digitalization of reading, proving that no proposals to rename library psychology to noocommunicology are tenable.
Learning Users’ Preferred Visual Styles in an Image Marketplace

Providing meaningful recommendations in a content marketplace is challenging due to the fact that users are not the final content consumers. Instead, most users are creatives whose interests, linked to the projects they work on, change rapidly and abruptly. To address the challenging task of recommending images to content creators, we design a RecSys that learns visual styles preferences transversal to the semantics of the projects users work on. We analyze the challenges of the task compared to content-based recommendations driven by semantics, propose an evaluation setup, and explain its applications in a global image marketplace.
Contour Measurement of Object with Arbitrary Surface Using Two-Dimensional Shearography with Source Displacement

A two-dimensional (2D) shearography with source displacement is proposed to measure object contours. Using a dual-shear shearographic setup with two movable laser sources, the full-field slopes along a pair of orthogonal shear directions were obtained. The contour was then obtained by performing 2D integration of the surface slopes. Theoretical derivations and experimental results are presented to demonstrate the performance of the proposed method. The experimental results show that contour of objects with various types of surfaces, such as spherical and hyperbolic paraboloid surfaces, can be effectively measured. The measurement of the contour aids in the precision measurement of strain and the precision location of defects.
Enhanced Security with Crystography Using AES and LSB

In the digital modern age, where all data is processed with respect to automated transfers and protection provision which are a major concern, the remaining reason for the intruders/hackers’ popularity is that much of the intelligence they receive from a device is they can interpret and can easily understand. Even if the data is encrypted, the attacker may quickly access the encrypted data and can use techniques such as the method of brute force and attempt to decrypt it and may even succeed in decrypting; it also reveals the information to other incognito users or alters the data to deceive a person or organization, or the knowledge gathered could be used to launch an attack. Appropriately, a technique for hiding sensitive messages during transmission is required. Communication of data between the parties while preserving its secrecy is a big problem everywhere, so a non-conventional solution to crystography is recommended to enhance the protection of shared data. Crystography is the advanced method of encryption approach in which steganography and cryptography are fused into two major methods. In this, cryptography is used for data scrambling, while steganography is to mask scrambled data in a file of image which provides two layers of encryption. The purpose of this is to concentrate on AES symmetric cryptography, its applications and techniques, and steganography of images is to provide the most convenient move of stego-image through a zip file format. In this paper, the advanced approach of images using histogram is used which conceals the hidden image recovery without failure of data.
Unsupervised learning on particle image velocimetry with embedded cross‐correlation and divergence‐free constraint

Particle image velocimetry (PIV) is an essential method in experimental fluid dynamics. In recent years, the development of deep learning-based methods has inspired new approaches to tackle the PIV problem, which considerably improves the accuracy of PIV. However, the supervised learning of PIV is driven by large volumes of data with ground truth information. Therefore, the authors consider unsupervised PIV methods. There has been some work on unsupervised PIV, but they are not nearly as effective as supervised learning PIV. The authors try to improve the effectiveness and accuracy of unsupervised PIV by adding classical PIV methods and physical constraints. In this paper, the authors propose an unsupervised PIV method combined with the cross-correlation method and divergence-free constraint, which obtains better performance than other unsupervised PIV methods. The authors compare some classical PIV methods and some deep learning methods, such as LiteFlowNet, LiteFlowNet-en, and UnLiteFlowNet with the authors’ model on the synthetic dataset. Besides, the authors contrast the results of LiteFlowNet, UnLiteFlowNet and the authors’ model on experimental particle images. As a result, the authors’ model shows comparable performance with classical PIV methods as well as supervised PIV methods and outperforms the previous unsupervised PIV method in most flow cases.
Tang Sancai Cultural and Creative Product Design Based on Bi4Ti3O12 Ferroelectric Nanomaterials

Nano has its small size and specific characteristics that are different from other materials, bringing a climax to the world’s on-site nano research and material development. In the ceramic industry, the nanometer is an important research object in the ceramic industry. This article aims to study nanomaterials and their application in Tang Sancai. The experiment in this paper shows from the Table that the sales volume of nanotechnology used in 2016-1020 is getting higher and higher, from 35.35 million yuan in 2016 to 10.43 million yuan in 2020, while the sales volume of ceramics that do not use nanotechnology has increased from 3567 in 2016. Ten thousand yuan has become 26.32 million yuan in 2020. It can be seen that ceramics using nanotechnology are more popular.
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference

Abstract This paper presents Diff-Explainer, the first hybrid framework for explainable multi-hop inference that integrates explicit constraints with neural architectures through differentiable convex optimization. Specifically, Diff- Explainer allows for the fine-tuning of neural representations within a constrained optimization framework to answer and explain multi-hop questions in natural language. To demonstrate the efficacy of the hybrid framework, we combine existing ILP-based solvers for multi-hop Question Answering (QA) with Transformer-based representations. An extensive empirical evaluation on scientific and commonsense QA tasks demonstrates that the integration of explicit constraints in a end-to-end differentiable framework can significantly improve the performance of non- differentiable ILP solvers (8.91%–13.3%). Moreover, additional analysis reveals that Diff-Explainer is able to achieve strong performance when compared to standalone Transformers and previous multi-hop approaches while still providing structured explanations in support of its predictions.
Performance Evaluation of Data Mining Classification Algorithms for Predicting Breast Cancer

The most prevalent cause of death among women is breast cancer. At an early stage, predicting breast cancer enhances the probability of a successful cure. It requires a breast cancer prediction technology capable of classifying a breast tumor as dangerous malignant or harmless benign. This is especially true in the medical field, where classification methods are often used for finding and investigation to make decisions for the disease. This study examines the performance of six classification algorithms of data mining which are Logistic Regression classifier, Naïve Bayes classifier, Decision Tree, Random Forest Classifier, Support Vector Machine, and K-Nearest Neighbors on the Wisconsin Breast Cancer (original) dataset. The principal purpose is to measure the performance of each algorithm in terms of their accuracy, precision, sensitivity, and specificity. The findings indicate that the accuracy of Support Vector Machine has the greatest rate (97.20 %) and the lowest error rate when determining if a woman has a malignant or benign tumor.
BG-EFRL: Chinese Named Entity Recognition Method and Application Based on Enhanced Feature Representation

Named Entity Recognition (NER) aims to mine meaningful entity information (eg, organization, place, person, etc.) in text. Due to the high dependence of Chinese NER on Chinese Word Segmentation (CWS), the negative impact of word segmentation errors in current models has to be bridged, but these models only focus on adjacent words and the words themselves. To this end, we propose a method that combines sentence context and text global relationship modeling (BG-EFRL), based on Transformer Encoder (BERT) and Graph Convolutional Network (GCN), with the help of the former self-attention weights to generate assist features to dynamically strengthen contextual representation. Experiments show that BG-EFRL is competitive on two real datasets, Weibo and Resume, and apply it to the food safety domain.
Hide and Seek: On the Stealthiness of Attacks Against Deep Learning Systems

With the growing popularity of artificial intelligence (AI) and machine learning (ML), a wide spectrum of attacks against deep learning (DL) models have been proposed in the literature. Both the evasion attacks and the poisoning attacks attempt to utilize adversarially altered samples to fool the victim model to misclassify the adversarial sample. While such attacks claim to be or are expected to be stealthy, i.e., imperceptible to human eyes, such claims are rarely evaluated. In this paper, we present the first large-scale study on the stealthiness of adversarial samples used in the attacks against deep learning. We have implemented 20 representative adversarial ML attacks on six popular benchmarking datasets. We evaluate the stealthiness of the attack samples using two complementary approaches: (1) a numerical study that adopts 24 metrics for image similarity or quality assessment; and (2) a user study of 3 sets of questionnaires that has collected 30,000+ annotations from 1,500+ responses. Our results show that the majority of the existing attacks introduce non-negligible perturbations that are not stealthy to human eyes. We further analyze the factors that contribute to attack stealthiness. We examine the correlation between the numerical analysis and the user studies, and demonstrate that some image quality metrics may provide useful guidance in attack designs, while there is still a significant gap between assessed image quality and visual stealthiness of attacks.
A Unified Approach to Path Planning of Multi-robot Systems Based on Bilevel Coordination Architecture

In this brief, a unified approach to path planning of multi-robot systems (MRS) is proposed based on bilevel coordination architecture. Such architecture is comprised of the global programming layer and local planning layer, which can be implemented by the improved particle swarm optimization (IPSO) and the dynamic window approach (DWA) respectively. Moreover, the nature and advantages of the proposed path planning algorithm are furthermore verified by the simulation experiments.
Grouped Adaptive Loss Weighting for Person Search

Person search is an integrated task of multiple sub-tasks such as foreground/background classification, bounding box regression and person re-identification. Therefore, person search is a typical multi-task learning problem, especially when solved in an end-to-end manner. Recently, some works enhance person search features by exploiting various auxiliary information, e.g. person joint keypoints, body part position, attributes, etc., which brings in more tasks and further complexifies a person search model. The inconsistent convergence rate of each task could potentially harm the model optimization. A straightforward solution is to manually assign different weights to different tasks, compensating for the diverse convergence rates. However, given the special case of person search, i.e. with a large number of tasks, it is impractical to weight the tasks manually. To this end, we propose a Grouped Adaptive Loss Weighting (GALW) method which adjusts the weight of each task automatically and dynamically. Specifically, we group tasks according to their convergence rates. Tasks within the same group share the same learnable weight, which is dynamically assigned by considering the loss uncertainty. Experimental results on two typical benchmarks, CUHK-SYSU and PRW, demonstrate the effectiveness of our method.
Toward Explainable Fashion Recommendation

Many studies have been conducted so far to build systems for recommending fashion items and outfits. Although they achieve good performances in their respective tasks, most of them cannot explain their judgments to the users, which compromises their usefulness. Toward explainable fashion recommendation, this study proposes a system that is able not only to provide a goodness score for an outfit but also to explain the score by providing reason behind it. For this purpose, we propose a method for quantifying how influential each feature of each item is to the score. Using this influence value, we can identify which item and what feature make the outfit good or bad. We represent the image of each item with a combination of human-interpretable features, and thereby the identification of the most influential item-feature pair gives useful explanation of the output score. To evaluate the performance of this approach, we design an experiment that can be performed without human annotation; we replace a single item-feature pair in an outfit so that the score will decrease, and then we test if the proposed method can detect the replaced item correctly using the above influence values. The experimental results show that the proposed method can accurately detect bad items in outfits lowering their scores.
A transdisciplinary approach to teaching and building a higher education system

Transdisciplinarity in higher education involves the formation of competence that enables the practical use of acquired knowledge, skills, and abilities from various disciplines in order to solve applied problems. The purpose of the academic paper lies in highlighting the features of using the transdisciplinary approach in the modern system of higher education. The present research is based on the transdisciplinarity concept as a way of solving complex social problems due to the properties and competencies of the individual to use knowledge, skills and abilities for solving complex problems. Results. Transdisciplinarity (TD) in the system of higher education is manifested as a form of education that ensures the formation of competence in specialists for solving complex problems based on a holistic approach and knowledge that is on the border of different disciplines. It has been revealed that TD is a new form of problem solving due to the specialist’s transdisciplinary knowledge and his competence to apply it to the existing problem in the subject area. Thus, transdisciplinarity in higher education is the formation of knowledge, skills and abilities in order to find a way for solving complex social problems. Higher educational institutions form transdisciplinary competence as a personal quality.
Taurus: Towards a Unified Force Representation and Universal Solver for Graph Layout

Over the past few decades, a large number of graph layout techniques have been proposed for visualizing graphs from various domains. In this paper, we present a general framework, Taurus, for unifying popular techniques such as the spring-electrical model, stress model, and maxent-stress model. It is based on a unified force representation, which formulates most existing techniques as a combination of quotient-based forces that combine power functions of graph-theoretical and Euclidean distances. This representation enables us to compare the strengths and weaknesses of existing techniques, while facilitating the development of new methods. Based on this, we propose a new balanced stress model (BSM) that is able to layout graphs in superior quality. In addition, we introduce a universal augmented stochastic gradient descent (SGD) optimizer that efficiently finds proper solutions for all layout techniques. To demonstrate the power of our framework, we conduct a comprehensive evaluation of existing techniques on a large number of synthetic and real graphs. We release an open-source package, which facilitates easy comparison of different graph layout methods for any graph input as well as effectively creating customized graph layout techniques.
Multi-Layered Graph Convolutional Network-Based Industrial Fault Diagnosis with Multiple Relation Characterization Capability

Fault diagnosis of industrial equipments is extremely important for the safety requirements of modern production processes. Lately, deep learning (DL) has been the mainstream fault diagnosis tool due to its powerful representational ability in learning and flexibility. However, most of the existing DL-based methods may suffer from two drawbacks: Firstly, only one metric is used to construct networks, thus multiple kinds of potential relationships between nodes are not explored. Secondly, there are few studies on how to obtain better node embedding by aggregating the features of different neighbors. To compensate for these deficiencies, an advantageous intelligent diagnosis scheme termed AE-MSGCN is proposed, which employs graph convolutional networks (GCNs) on multi-layer networks in an innovative manner. In detail, AE is carried out to extract deep representation features in process measurement and then combined with different metrics (i.e., K-nearest neighbors, cosine similarity, path graph) to construct the multi-layer networks for better multiple interaction characterization among nodes. After that, intra-layer convolutional and inter-layer convolutional methods are adopted for aggregating extensive neighbouring information to enrich the representation of nodes and diagnosis performance. Finally, a benchmark platform and a real-world case both verify that the proposed AE-MSGCN is more effective and practical than the existing state-of-the-art methods.