Reviewerly/exHarmony huggingface dataset:
Fast Edit Propagation for 360 Degree Panoramas Using Function Interpolation

Due to its intuitive stroke-based user interface, edit propagation has wide applications such as image recoloring and relighting, tone editing, image matting, etc.This paper presents a function interpolation method for efficient edit propagation on 360 degree panoramas.Given a panoramic image as input and user-specified strokes as edit hints, our approach adopts an adaptive sampling strategy based on the diversity of local color distribution.By selecting the most representative samples, this strategy can significantly reduce the number of samples.We assume that the projection of the input panorama is known, so that it can be mapped to a 3D unit sphere where the spherical distance metric can be defined.Then edit propagation is formulated as a radial basis function interpolation problem, which can be efficiently solved by using the non-negative linear least squares.We also utilize a multi-resolution strategy and a function look-up table for further acceleration.Experimental results show that our method can efficiently propagate edits on high-resolution panoramas and produce seam-free and smooth editing results.
SPE$^{2}$: Self-Paced Ensemble of Ensembles for Software Defect Prediction

Software defect prediction aims to predict defect-prone code regions automatically before defects are discovered. Accurate prediction helps software practitioners to prioritize their testing efforts. In recent decades, dozens of approaches have been put forward and acquired good results in this field. However, in practical scenarios, many projects have limited labeled instances; more than that, most of these labeled instances are nondefective. The lack of training data and class imbalance problem together bring serious challenges to software defect prediction tasks. So far, few of prevailing approaches can well handle these two difficulties simultaneously. One important reason is that they do not pay adequate attention to several key instances, which are difficult to classify in a small imbalanced dataset. This article introduces the concept of “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">instance hardness</i> ” to integrate various difficulties of imbalance classification tasks. Based on it, a novel imbalance learning framework named self-paced ensemble of ensembles (SPE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> ) is proposed to perform software defect prediction. SPE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> aims to generate a strong ensemble of ensembles by self-paced harmonizing instance hardness via undersampling. Finally, SPE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> is extensively compared with eight imbalance learning approaches on ten open-source defect datasets. Experiments indicate that SPE <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> improves the performance and achieves better and more significant F-measure values than its existing counterparts, based on Brunner’s statistical significance test and Cliff’s effect sizes.
Algorithm Reliance Under Pressure: The Effect of Customer Load on Service Workers

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Resilient Bioinspired Algorithms: A Computer System Design Perspective

Resilience can be defined as a system's capability for returning to normal operation after having suffered a disruption. This notion is of the foremost interest in many areas, in particular engineering. We argue in this position paper that is a crucial property for bioinspired optimization algorithms as well. Following a computer system perspective, we correlate some of the defining requirements for attaining resilient systems to issues, features, and mechanisms of these techniques. It is shown that bioinspired algorithms do not only exhibit a notorious built-in resilience, but that their plasticity also allows accommodating components that may boost it in different ways. We also provide some relevant research directions in this area.
Blockvoting:An Online Voting System Using Block Chain

The voting mechanism is extremely important in a democratic country like India. And we all know that any flaw in the voting mechanism will raise serious concerns about the entire electoral process. Creating a crowd in the existing scenario of Covid-19 also adds a lot of complications. As a result, in such a situation, the online voting system will be a huge success in the election. However, the online system&#x2019;s security and transparency raise certain concerns. So incorporating blockchain into online E-voting will eliminate all of these flaws. The method allows voters to register and vote for any candidate. The vote will be saved in a secure block chain, but all other information, such as the voter&#x2019;s name, city, and whether they voted or not, will be accessible to anybody via the website. This system will provide security by denying duplication of votes.
Deep recurrent Gaussian Nesterovs recommendation using multi-agent in social networks

Due to increasing volume of big data the high volume of information in Social Network put a stop to users from acquiring serviceable information intelligently so many recommendation systems have emerged. Multi-agent Deep Learning gains rapid attraction, and the latest accomplishments address problems with real-world complexity. With big data precise recommendation has yet to be answered. In proposed work Deep Recurrent Gaussian Nesterov's Optimal Gradient (DR-GNOG) that combines deep learning with a multi-agent scenario for optimal and precise recommendation. The DR-GNOG is split into three layers, an input layer, two hidden layers and an output layer. The tweets obtained from the users are provided to the input layer by the Tweet Accumulator Agent. Then, in the first hidden layer, Tweet Classifier Agent performs optimized and relevant tweet classification by means of Gaussian Nesterov's Optimal Gradient model. In the second layer, a Deep Recurrent Predictive Recommendation model is designed to concentrate on the vanishing gradient issue arising due to updated tweets obtained from same user at different time instance. Finally, with the aid of hyperbolic activation function in the output layer, building block of the predictive recommendation is obtained. In the experimental study the proposed method is found better than existing GANCF and Bootstrapping method 13-21% in case of recommendation accuracy, 22-32% better in recommendation time and 15-22% better in recall rate.
SAM: Self Attention Mechanism for Scene Text Recognition Based on Swin Transformer

Scene text recognition, which detects and recognizes the text in the image, has engaged extensive research interest. Attention mechanism based methods for scene text recognition have achieved competitive performance. For scene text recognition, the attention mechanism is usually combined with RNN structures as a module to predict the results. However, RNN attention-based methods are sometimes hard to converge on account of gradient vanishing/exploding during training, and RNN cannot be computed in parallel. To remedy this issue, we propose a Swin Transformer-based encoder-decoder mechanism, which relies entirely on the self attention mechanism (SAM) and can be computed in parallel. SAM is an efficient text recognizer that is only formed by two components: 1) an encoder based on Swin Transformer that gets the visual information of input image, and 2) a Transformer-based decoder gets the final results by applying self attention to the output of encoder. Considering that the scale of scene text has a large variation in images, we apply the Swin Transformer to compute the visual features with shifted windows, which permits self attention computation to cross-window connections and limits for non-overlapping local window. Our method has improved in accuracy over previous methods at ICDAR2003, ICDAR2013, SVT, SVT-P, CUTE and ICDAR2015 by 0.9%, 3.2%, 0.8%, 1.3%, 1.7%, 1.1% respectively. Especially, our method achieved the fastest predict time of 0.02s per image.
A Distance Measure for Privacy-Preserving Process Mining Based on Feature Learning

To enable process analysis based on an event log without compromising the privacy of individuals involved in process execution, a log may be anonymized. Such anonymization strives to transform a log so that it satisfies provable privacy guarantees, while largely maintaining its utility for process analysis. Existing techniques perform anonymization using simple, syntactic measures to identify suitable transformation operations. This way, the semantics of the activities referenced by the events in a trace are neglected, potentially leading to transformations in which events of unrelated activities are merged. To avoid this and incorporate the semantics of activities during anonymization, we propose to instead incorporate a distance measure based on feature learning. Specifically, we show how embeddings of events enable the definition of a distance measure for traces to guide event log anonymization. Our experiments with real-world data indicate that anonymization using this measure, compared to a syntactic one, yields logs that are closer to the original log in various dimensions and, hence, have higher utility for process analysis.
A bottom-up GIS-based method for simulation of ground-mounted PV potentials at regional scale

Solar photovoltaic (PV) is a key technology for any renewable energy system. As subsidy-free PV becomes more and more economically feasible, region-specific planning tools that define areas suitable for ground-mounted PV are needed. While many top-down studies have assessed suitable areas at a national scale, an accurate scalable bottom-up assessment of regional ground-mounted PV potentials in high spatial and temporal resolution that goes further than a mere identification of appropriate land areas is missing. This work introduces such a method based on digital landscape models that consider terrain slope, orientation, location-specific irradiation, and land use type, and combines this geoinformatical information with a PV yield model that allows to assess hourly PV generation potential on suitable areas. The method is validated with three existing ground-mounted PV plants in Germany, where a comparison of real and simulated annual electricity yields shows average deviations of 5%. Subsequently, ground-mounted PV potentials in three German counties with varying settlement structures as well as topographic and weather patterns are assessed and a comparison of yearly and hourly simulated generation potentials with regional electricity demand is performed. While the yearly analysis demonstrates the substantial overall potentials of local ground mounted-PV in all regions, with demand coverages ranging from 80% to hypothetically more than 40 times of current electricity demand according to current regulations, the hourly autarky ratio, defined as the share of hours of a year where ground-mounted PV can satisfy demand, ranges from 25% to 40%, without consideration of storage or demand side management. A subsequent investigation of the ability to export excess electricity generation from ground-mounted PV shows that the two regions with highest ground-mounted PV potentials have less-developed grid infrastructures, thus restricting excess electricity generation export potentials.
Impact of Agile Scrum Methodology on Team’s Productivity and Client Satisfaction – A Case Study

Customer satisfaction is the primary goal of any software service provider in the long run. A small team dedicated to a short sprint handles a variety of challenges while providing deliverables to the client in agile software development. The customer, on the other hand, makes numerous decisions to get a design that meets market standard. In Agile software development, changes are organized into user stories for the sprint, developed in iterations and are reviewed by customers on regular intervals. Under the supervision of Scrum master, the teams work in back-to-back sprints to meet the sprints goals. Agile helps to deliver the end product in line to the client requirements but team might feel stressed out in the end-to-end process. This research paper presents the performance analysis of Agile Scrum methodology towards critical project parameters of Customer satisfaction and Team’s productivity.
Robust deep kernel-based fuzzy clustering with spatial information for image segmentation

Clustering algorithms with deep neural network has attracted wide attention to scholars. A deep fuzzy K-means clustering algorithm model on adaptive loss function and entropy regularization (DFKM) is proposed by combining automatic encoder and clustering algorithm. Although it introduces adaptive loss function and entropy regularization to improve the robustness of the model, its segmentation effect is not ideal for high noise. The research purpose of this paper is to focus on the anti-noise performance of image segmentation. Therefore, on the basis of DFKM, this paper focus on image segmentation, combine neighborhood median and mean information of current pixel, introduce neighborhood information of membership degree, and extend Euclidean distance to kernel space by using kernel function, propose a dual-neighborhood information constrained deep fuzzy clustering based on kernel function (KDFKMS). A large number of experimental results show that compared with DFKM and classical image segmentation algorithms, this algorithm has stronger anti-noise robustness.
Solar Irradiation Forecasting - Comparative Analysis of Various Methods

The global energy system is transforming with the consideration of a growing population with rising living standards that will need more energy. Simultaneously, the world must find ways to reduce greenhouse gas emissions and provide sustainable energy production. As electricity is the fastest-growing part of the energy system and thus shifting to renewable energy production systems is the need of the hour. IEA reports that the Sun could be one of the largest resources for energy production for the upcoming ‘net zero goals’ of the world. Rapid decrease in the cost of installation and availability of solar radiation are major advantages for adoption, although this mode of generation of energy has a variable output which is influenced by several parameters. Hence solar energy forecasting is crucial to understand and predict the output of production, the demand of the consumers, and optimizing the dispatch of the electricity from grids to users. In this paper, we have analyzed innovative methodologies for forecasting solar energy radiation. Moreover, it provides a review and comparative analysis on various Artificial Neural Networks (ANN), Probabilistic and Statistical, and Time-Series based models used for estimating solar irradiation. Each of these models have been trained and tested in different geographical regions, weather conditions, and corresponding relevant parameters, which would provide an insight on efficient model selection and optimization for predicting short and long-term solar irradiation. Further, we have discussed some challenges while forecasting solar irradiation and future research direction in this domain.
Adoption of Blockchain Technology in the Indian Business Market: Obstacles and Opportunities

Blockchain is a decentralized information and transaction management technology initially implemented for Bitcoin. Thus, attractive research areas, particularly from the angle of technical obstacles and adoption limitations, arise. They are thus giving rise to the need for this research. The chapter aims to recognize (RQ1) various industries in which blockchain tech is being used (which is discussed with the help of past use cases.) and (RQ2) the multiple obstacles due to which blockchain implementations are still weak in India. Authors extracted 45 papers cautiously to find out the benefits and use cases to support our study. At the same time, the questionnaire was floated to find the various obstacles, due to which blockchain implementations are still weak in India. Results reveal that 56% of respondents agree “time-saving” is the most significant benefit, and “lack of experts skilled in blockchain technology” is the biggest adoption challenge with 73% response. It had been found that the current acceptance of blockchain technology was low to moderate using technology acceptance model (TAM). This chapter presents a summary of potential applications and uses cases/applications of blockchain to help companies eliminate this risk. The majority of this conceptual research specializes in improving knowledge of blockchain technology. “Time-saving” was the most significant benefit, with 56% of respondents feeling the same, followed by “better data protection” and “reduction of risk” with a vote share of about 54%. The research carried out in this article is an original work. It contributes a new piece of knowledge that has not previously been presented.
Video Frame Deletion and Duplication

Abstract Videos can be manipulated in a number of different ways, including object addition or removal, deep fake videos, temporal removal or duplication of parts of the video, etc. In this chapter, we provide an overview of the previous work related to video frame deletion and duplication and dive into the details of two deep-learning-based approaches for detecting and localizing frame deletion (Chengjiang et al. 2017) and duplication (Chengjiang et al. 2019) manipulations.
Approximate Bipartite Graph Matching by Modifying Cost Matrix

Graph matching is the process of evaluating the structural similarity between the two graphs. Bipartite graph matching is one of the important technique for error-tolerant graph matching. In this paper, we present an approach to bipartite graph matching by considering the edge assignment instead of node assignment during the creation of cost matrix. We demonstrate this technique can achieve better accuracy on some graph dataset.
An Effective Crowdsourced Test Report Clustering Model Based on Sentence Embedding

In the crowdsourced testing industry, efficient and automated classification of true bugs from test reports can greatly reduce the cost of software testing. Most of the existing methods are based on TF-IDF or machine learning methods to vectorize the test report and then construct a classifier. However, the document vector constructed by keywords more or less ignores the description information in the document, which affects the performance of classification and detection of real defects. In order to use the description information to construct an effective report clustering model, we propose a model called RCSE to encode test report description information at the sentence level, calculate the similarity between the test reports from the feature similarity of the description sentence, then cluster the test report. We evaluated the model on 3,442 reports. The experimental results show that the clustering model based on sentence embedding has an average purity of 12.3 % and an ARI of 22.0% higher than the keyword-based model on three report datasets.
The Third-Generation University Ecosystem in the Context of Global Digitalization

The modern university is fully becoming the conductor of the ecosystem, providing not only a wide range of research and educational services, but also comprehensive innovative products and IT services for a comfortable educational process and scientific research at a high global level. This paradigm shift from business competition to ecosystem competition is not only due to the digitalization of Industry 4.0, but also to the acceleration of human communication, exchange of goods, and knowledge. The research attempts to review the features of functioning of the ecosystem of the university in the digital economy 4.0, to study its mission and the conditions and principles of sustainable development, the features of building a value chain created by the third generation university in a digital economy. The research results can be used in the elaboration of the value chain of research and academic services in a digital economy, as well as in the formation of IT-infrastructure and clarification of the mission of the third generatnio university to respond to the global challenges.
Resilient Biomedical Systems Design Under Noise Using Logic-Based Machine Learning

Increased reliance on electronic health records and plethora of new sensor technologies has enabled the use of machine learning (ML) in medical diagnosis. This has opened up promising opportunities for faster and automated decision making, particularly in early and repetitive diagnostic routines. Nevertheless, there are also increased possibilities of data aberrance arising from environmentally induced noise. It is vital to create ML models that are resilient in the presence of data noise to minimize erroneous classifications that could be crucial. This study uses a recently proposed ML algorithm called the Tsetlin machine (TM) to study the robustness against noise-injected medical data. We test two different feature extraction methods, in conjunction with the TM, to explore how feature engineering can mitigate the impact of noise corruption. Our results show the TM is capable of effective classification even with a signal-to-noise ratio (SNR) of −15dB as its training parameters remain resilient to noise injection. We show that high testing data sensitivity can still be possible at very low SNRs through a balance of feature distribution–based discretization and a rule mining algorithm used as a noise filtering encoding method. Through this method we show how a smaller number of core features can be extracted from a noisy problem space resulting in reduced ML model complexity and memory footprint—in some cases up to 6x fewer training parameters while retaining equal or better performance. In addition, we investigate the cost of noise resilience in terms of energy when compared with recently proposed binarized neural networks.
A Task-oriented Chatbot Based on LSTM and Reinforcement Learning

Thanks to the advancements in deep learning, chatbots are widely used in messaging applications. Undoubtedly, a chatbot is a new way of interaction between humans and machines. However, most of the chatbots act as a simple question answering system that responds with formulated answers. Traditional conversational chatbots usually adopt a retrieval-based model that requires a large amount of conversational data for retrieving various intents. Hence, training a chatbot model that uses low-resource conversational data to generate more diverse dialogues is desirable. We propose a method to build a task-oriented chatbot using a sentence generation model that generates sequences based on the generative adversarial network. The architecture of our model contains a generator that generates a diverse sentence and a discriminator that judges the sentences by comparing the generated and the ground-truth sentences. In the generator, we combine the attention model with the sequence-to-sequence model using hierarchical long short-term memory to extract sentence information. For the discriminator, our reward mechanism assigns low rewards for repeated sentences and high rewards for diverse sentences. Extensive experiments are presented to demonstrate the utility of our model that generates more diverse and information-rich sentences than those of the existing approaches.