Reviewerly/exHarmony huggingface dataset:
BL-GAN: Semi-Supervised Bug Localization via Generative Adversarial Network

Various automated bug localization technologies have recently emerged that require adequate bug-fix records available to train a predictive model. However, many projects in practice might not provide these necessities, especially for new projects in the first release, due to the expensive human effort for constructing a large amount of bug-fix records. Aiming to capture the potential relevance distribution between the bug report and code file from a limited number of available bug-fix records, we present the first semi-supervised bug localization model named BL-GAN in this paper. For this purpose, the promising Generative Adversarial Network is introduced in BL-GAN, in which synthetic bug-fix records close to the real ones are constructed by searching the project directory tree to generate file paths instead of traversing the contents of all code files. For processing bug reports, the proposed BL-GAN adopts an attention-based Transformer architecture to capture semantic and sequence information. In order to capture the proprietary structural information in code files, BL-GAN incorporates a novel multilayer Graph Convolutional Network to process the source code in a graphical view. Extensive experiments on large-scale real-world datasets reveal that our model BL-GAN significantly outperforms the state-of-the-art on all evaluation measures.
Model of the development of productive experience in children of primary school age in additional education

The relevance of the problem under study is due to the aggravation of contradictions between the objective need of society for a culturally productive personality and the lack of knowledge about the content of productive experience as one of the targets of modern education; between the wide possibilities of additional education in the development of productive experience and the insufficient degree of realization of its potential in relation to the considered area of pedagogical research. The aim of the study is to design a model for the development of productive experience in children of primary school age in additional education. Research methods: analytical review of scientific sources, modeling. The model ensures the realization of the potential of additional education in the development of the structural components of productive experience in children of primary school age. Particular attention is paid to the study of the procedural component of the model, aimed at the use in practice of traditional and innovative methods, techniques, tools, the implementation of pedagogical conditions that ensure the effectiveness of the process under study.
Massive Data Language Models and Conversational AI: Emerging Issues

Google’s LaMDA, Open AI’s GPT-3 and Meta’s BlenderBot are artificially intelligent (AI) based chatbots, that have been trained on billions of documents creating the notion of “Massive Data.” These systems use human generated documents to capture words and relationships between words that people use when they communicate. This paper examines some of the similarities of these systems and examines some of the emerging issues regarding these massive data language models, including whether they are sentient, the use and impact of scale, information use and ownership, explanation of discussion and answers and other concerns. This paper also directly investigates some artifacts of Google’s LaMDA, and compares it with Meta’s BlenderBot. Finally, this paper examines some emerging issues and questions deriving from our analysis.
Specific Emitter Identification Based on Homomorphic Filtering and Support Vector Machine-2K

Specific emitter identification (SEI) is the process of identifying or discriminating different emitters by extracting the radio frequency fingerprints from the received signals. A novel SEI scheme with two steps is proposed in this paper. In the first step, the new fingerprint features are extracted as the emitter-irrelated information is suppressed by homomorphic filtering. Then, Two View SVM-2K (Support Vector Machine on two Kernels) classifier is exploited to classify emitters effectively based on the above features. Simulation results show that the proposed method achieved better classification performance than the benchmark method.
Normalizing Gradients

As stated previously, normalizing activations and weights aims to provide a better optimization landscape for DNNs, by satisfying Criteria 1 and 2 in Chap. 2. Rather than providing a good optimization landscape by design, normalizing gradients in DNNs aims to exploit the curvature information for GD/SGD, even though the optimization landscape is ill-conditioned.
Synergetic Effects of the Digital Transformation of Educational Services

The development of educational services during the transition to the digital economy has been gaining much scholarly attention recently. One of the questions discussed in this regard is the positive and negative synergetic effects arising from the transformation of educational services. This study aims to develop a methodology for assessing the synergetic effects of the digital transformation in educational services. The proposed methodology is a synthesis of the graphical method and the method of grouping the data on digital services in Russian higher education, according to a set of criteria (the number of higher education institutions, the number of educational programs, the proportion of e-learning programs, the number of students enrolled in educational programs, and the number of students in e-learning programs). The study traces the dynamics of the synergetic effects of the Russian digital services system in 2016-2020 and identifies the factors that have a negative impact on the development of this system. The study also describes the main trends in the development of higher education and digital educational services and examines the correlation of these trends with the changes in the synergetic effects. Based on the research findings, recommendations for further development of digital educational services in the Russian higher education system are given.
DirectVoxGO++: Fast Neural Radiance Fields for Object Reconstruction

In recent years, new reconstruction techniques based on Neural Radiance Fields (NeRFs) have created new forms to model objects instead of the traditional mesh and point cloud-based representations, allowing for more photorealistic rendering. However, these techniques were too slow to be used in practical settings, taking in the range of hours in high-end GPUs. Due to these limitations, new techniques have been created for fast reconstruction of scenes, such as DirectVoxGO. Alongside this limitation, one issue with NeRFs is that they were initially unable to separate the foreground from the background and had problems with 360 scenes until the emergence of new techniques such as NeRF++. Our method extends DirectVoxGO to allow the handling of unbounded scenes inspired by some ideas from NeRF++, adapting it to incorporate elements from a neural hashing approach employed by other works. Our technique improved photorealism compared with DirectVoxGO and Plenoxels on a subset of the LF dataset on average in at least 2%, 8%, and 8% for PSNR, SSIM, and LPIPS metrics, respectively, while also being an order of magnitude faster than NeRF++. Code will be available in https://github.com/danperazzo/dvgoplusplus.
How important are user-generated data for search result quality? Experimental evidence

Do some search engines produce better search results because their algorithm is better, or because they have access to more data from past searches? In the latter case, mandatory data sharing, a policy that is currently discussed, could trigger innovation and would benefit all users of search engines. We document that the algorithm of a small search engine can produce non-personalized results that are of similar quality than Googleâ€™s, if it has enough data, and that overall differences in the quality of search results are explained by searches for less popular search terms. This is confirmed by results from an experiment, in which we keep the algorithm of the search engine fixed and vary the amount of data it uses as an input.
Edge-preserving Image Smoothing via Counting-weighted Total Variation

We present a new counting-weighted total variation measure, which captures the consistency of gradient directions in local regions to distinguish edges and details. A novel optimization framework with the proposed counting-weighted total variation in the l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> regularization term is then developed to realize the edge-preserving image smoothing. In order to solve the optimization problem, we adopt an iteratively re-weighted least square based algorithm. Experimental results demonstrate that the proposed method is capable of completing edge-preserving image smoothing, while avoiding blurring and over-sharpening the edge. The proposed method can also be used for the image detail enhancement without involving halos or gradient reversal artifacts, while achieve better quality scores in the comparison with other enhancement methods.
Using Tangible Modeling to Create an e $$^{3}$$ value Conceptual Model for Digital Ecosystems

The design of digital platforms and ecosystems is a challenging problem that involves technical, organizational, and business aspects. In this context, modeling languages for creating conceptualizations are crucial to support the design of comprehensive ecosystems. However, the creation of conceptual models requires the input and expertise of practitioners, who usually do not have the time to learn modeling languages. In this paper, we explore how to overcome this challenge by combining a tangible design approach for ecosystems, named the Tangible Ecosystem Design (TED) methodology, with a value modeling approach named the e $$^{3}$$ value methodology. We created and analyzed a TED Service Map and a corresponding e $$^{3}$$ value model to formulate lessons learned based on a comparison of models. Our results suggest that (1) the design process of a value model can benefit from tangible modeling and (2) model elements can be partially transferred from TED models to e $$^{3}$$ value models. The contribution of this paper provides lessons learned that can be used to derive e $$^{3}$$ value models from the TED approach and thereby lower entry barriers for conceptual models of complex ecosystems.
Plant Landscape Configuration Method of Regional Characteristic Rainwater Garden Based on Deep Learning

With the acceleration of urbanization and the continuous expansion and construction of cities, more and more cities in China are facing increasingly serious urban rainwater problems. As a rainwater management measure under the low impact development system, rainwater garden can manage and utilize rainwater resources. Therefore, a plant landscape configuration method of regional characteristic rainwater garden based on in-depth learning is proposed. By analyzing the types and characteristics of rainwater landscape facilities; Constructed rainwater garden runoff management system; Design the plant configuration scheme of wet area, semi-humid area, arid area and semi-arid area of rainwater garden; The plant landscape characteristic parameters are calculated based on the deep learning algorithm to complete the plant landscape configuration. Experiments show that the waterlogging tolerance index D of this rainwater garden landscape configuration method for different kinds of plant landscape is higher than that of the traditional configuration method, which can manage rainwater and improve the ecological environment at the same time.
Uncertainty-Aware Deep Open-Set Object Detection

Open-set object detection better simulates the real world compared with close-set object detection. Besides the classes of interest, it also pays attention to unknown objects in the environment. We extend the previous concept of open-set object detection, aiming to detect both known and unknown objects. Because unknown objects have different textural features from known classes and the background, we assume that detecting unknown instances will generate high uncertainty. Therefore, in this paper, we propose an uncertainty-aware open-set object detection framework based on faster R-CNN. We introduce evidential deep learning to the field of object detection to estimate the uncertainty of the predictions and perform more accurate classification in open-set conditions. The obtained uncertainty will be utilized to pseudo-label unknown instances in the training data. We also introduce a contrastive clustering module to separate the feature representations of each class during the training phase. We set an uncertainty-based unknown identifier at the inference phase to enhance the generalization of the detector. We conduct experiments on three different data splits, and our method outperforms the recent SOTA method. We also demonstrate each component in our method is effective and indispensable in our ablation studies.
Driving Scene Supplementary Class Balancing Network for Edge Driving Scene Recognition

Driving scene data in the natural environments show an extremely imbalanced distribution, i.e. some scenes are in the majority while others are very rare. These rare driving scenes, also known as edge driving scenes, can dramatically affect the accuracy of driving scene recognition, as well as the safety, convenience, and intelligence of autonomous driving. Thus, this paper proposed the Driving Scene Supplementary Class Balancing Network to address edge driving scene recognition, which can effectively recognize edge driving scenes while training in imbalanced data. Our approach consists of three components. First, the Edge Scene Supplementer generates extremely rare scenes to supplement the training data to reduce the excessive imbalance between edge scenes and general scenes. Then, the Feature Encoder is responsible for efficiently extracting visual and temporal representations from the driving scenes. Finally, the Weight Balancing Classifier presents the classification results and re-balances the loss of driving scenes. To evaluate the performance of the proposed method, comprehensive experiments were performed on two datasets of real-world driving scenes. The results show that the proposed method outperforms the state-of-the-art methods in edge driving scene recognition.
ChainSCAN: A Blockchain-Based Supply Chain Alerting Framework for Food Safety

Supply Chain Management (SCM) systems provide a digital platform for the supply chain organizations to communicate and exchange information. In the food industry, SCMs provide the essential software components for monitoring food transportation and product handling. However, contemporary SCM systems suffer from non-traceability, non-interoperability and poor resiliency. Global food supply chains are at risks from cyber threats, which will lead to food shortages and poor quality control. In this paper, we present ChainSCAN, a three-layer design of a fully decentralized and blockchain-based SCM system that protects data security, privacy, and remove single point of failures of traditional SCMs. In addition, ChainSCAN provides a transparent environment to protect physical security of assets from stolen and record all activities of the food supply chain. ChainSCAN leverages four smart contracts to automate and regulate interactions of supply chain participants with the ledger, which can enforce the best practices among all supply chain organizations. Furthermore, ChainSCAN contains a notification system that automatically alerts consumers about food recalls and/or potential misbehaviors.
Single document extractive text summarization using cuckoo search algorithm

In today’s world, the rapid growth of text data on web and online resources makes a challenging problem for the human beings to get the essential information. Finding such information can only be feasible by summarizing the text, known as Text summarization (TS). TS is the process of compressing the original text document without losing the core contents. Hence the most conventional and tricky method for summarization is to fetch the most informative or representative sentences from the original input document which is well-known as Extractive Text Summarization. It has the potential to obtain the valuable information in the shortest period of time. In this paper, three different nature-inspired algorithms such as Cuckoo search algorithm (CS), Firefly algorithm (FF) & Flower pollination algorithm (FP) are used to generate the summary for a document. The implementation is done over DUC 2003 dataset. However, the CS algorithm-based model is showing significantly better result than the other two models for this problem.
A Three-bit Image Stegnography for Enhancing Image Quality in Medical Application

In comparison to conventional methods, the internet's infrastructure has undergone enormous advancements. Consequently, special caution and protection are required for the very private medical records of patients. To better the imperceptibility and data-hiding capabilities of steganographic images, we have developed an upgraded approach. Steganography is the art of concealing information and a method used to deny that information is even embedded. It is a more effective method of communication security than encryption, which merely hides the letter's content and not its presence. The original message is concealed within a carrier so that any changes to the payload are imperceptible. In this article, we'll talk about how communications can be concealed using digital images as a carrier. Additionally, the effectiveness of several steganography tools is examined in this research. A helpful technology for secret data transmission via a communication platform is steganography. The concealed imaging is produced by fusing the carrier image and secret image. It is challenging to find the hidden images without retrieving.
Python Web Development

Python is widely used for developing large-scale web applications that are not possible to build using .NET and PHP.
Application of Deep Convolutional Neural Network in Image Recognition

This article studies the use of a convolutional neural network for image recognition. Since neural networks at this time are widely used in recognizing computer images and have good results, further study and use of the neural network have tremendous relevance. In this article, we have made an analysis of the accuracy of image recognition using two different optimizers Adam and RMSProp, where the pictures of the authors of the article was presented as input data and several steps were performed for the implementation, which the article describes in detail. This article focuses on the RMSProp and Adam optimizers as they are adaptive and work in a large number of scenarios. The results in the form of tables and graphs were presented.
Long-term Prediction of Time Series Based on Fuzzy Cognitive Map And Ensemble Learning

Fuzzy cognitive maps (FCM) have been used as a recognized and powerful knowledge modelling tool for time series modelling and forecasting. The method performs well in single-step forecasting or short-term forecasting, but does not perform well in the field of long-term forecasting due to the complex interrelationships that may exist between the different subsequent steps. This paper proposes an FCM-based long-term time series forecasting model that combines time series granulation, fuzzy C-mean clustering, ensemble learning, and FCM. Firstly, time series are granulated into suitable segments and are isometric, and then these isometric segments are transformed into fuzzy time series and clustering centres using fuzzy C-mean clustering. The BAFCM model is built by combining the Bagging method of ensemble learning and FCM. Subseries of the fuzzy time series are selected randomly using Bootstrap, and then a separate FCM-based learner is used for each subsequence, and each FCM-based learner is learned quickly and robustly based on a convex optimisation method. The output of multiple base learners is then combined using a weighted average method to obtain the predicted output of the fuzzy time series, which is finally extrapolated together with the clustering centres to achieve long-term prediction of the time series. To demonstrate the performance of our method in long-term prediction, we tested it on synthetic and real-life time series and compared it with other representative prediction methods. The experiments demonstrate the superiority of our method in the long-term prediction of time series.
Design and Implementation of Online Education Intelligent Learning System under the Background of Big Data

With the advent of the Internet big data era, people are paying more and more attention to education. The traditional learning model can no longer meet people's needs, and the learning resources under big data are characterized by diversification and enrichment. Combining big data network learning resources and an intelligent online education system can effectively break the time and space limitations under the traditional teaching mode, so learning is not limited to the classroom. The intelligent learning system technology designed and implemented in this article mainly uses J2EE technology for Web-based application development. It uses the current mainstream MVC JFinal framework to split the system into a three-tier middle-tier architecture and a data service layer. The MVC (Model-View-Controller) function is a classic architecture design function that divides the entire software system into three parts: model, viewer, and controller. The learning system can fully meet the individual needs of learners with different learning foundations. The research results show that the scores in the four aspects of learning interest, knowledge acquisition, skill acquisition and extended application are 3.8 and 3.6, 4.5 and 4.1, 4.6 and 3.9, and 4.7 and 4.4, respectively. The intelligent online classroom model is higher than the traditional classroom in four indicators. The intelligent online classroom has significant advantages and is of great help to meet students' individual needs.
A Statistical Approach for Multi-frame Shadow Movement Detection and Shadow Removal for Document Capture

Shadow detection is an integral part of scanned document image processing. In this paper, we have introduced a new technique for identifying shadow movement regions in documents. Provided a sequence of image frames, our algorithm identifies documents from an image using a boundary detection algorithm followed by computing their respective perspective corrected images. Further, intensity map images are computed from the perspective corrected images. Finally, a statistical analysis is carried out on the different blocks of the intensity maps to estimate the shadow movement regions. The identified blocks along with a shadow removal algorithm are used to remove shadows from the documents. The proposed algorithm has been tested in with the Adobe Scan application and the experimental results show the efficacy of the proposed algorithm. The algorithm has a low computation complexity which makes it suitable to use in different mobile applications.