Reviewerly/exHarmony huggingface dataset:
A Conversation-Driven Approach for Chatbot Management

Managing and evolving a chatbot’s content is a laborious process and there is still a lack of standardization. In this context of standardization, the absence of a management process can lead to bad user experiences with a chatbot. This work proposes the Chatbot Management Process, a methodology for content management on chatbot systems. The proposed methodology is based on the experiences acquired with the development of Evatalk, the chatbot for the Brazilian Virtual School of Government. The focus of this methodology is to evolve the chatbot content through the analysis of user interactions, allowing a cyclic and human-supervised process. We divided the proposed methodology into three distinct phases, namely, manage, build, and analyze. Moreover, the proposed methodology presents a clear definition of the roles of the chatbot team. We validate the proposed methodology along with the creation of the Evatalk chatbot, whose amount of interactions was of 22,771 for the 1,698,957 enrolled attendees in the Brazillian Virtual School of Government in 2020. The application of the methodology on Evatalk’s chatbot brought positive results: we reduced the chatbot’s human hand-off rate from 44.43% to 30.16%, the chatbot’s knowledge base examples increased by 160% whilst maintaining a high percentage of confidence in its responses and keeping the user satisfaction collected in conversations stable.
Sablas: Learning Safe Control for Black-Box Dynamical Systems

Control certificates based on barrier functions have been a powerful tool to generate probably safe control policies for dynamical systems. However, existing methods based on barrier certificates are normally for white-box systems with differentiable dynamics, which makes them inapplicable to many practical applications where the system is a black-box and cannot be accurately modeled. On the other side, model-free reinforcement learning (RL) methods for black-box systems suffer from lack of safety guarantees and low sampling efficiency. In this paper, we propose a novel method that can learn safe control policies and barrier certificates for black-box dynamical systems, without requiring for an accurate system model. Our method re-designs the loss function to back-propagate gradient to the control policy even when the black-box dynamical system is non-differentiable, and we show that the safety certificates hold on the black-box system. Empirical results in simulation show that our method can significantly improve the performance of the learned policies by achieving nearly 100% safety and goal reaching rates using much fewer training samples, compared to state-of-the-art black-box safe control methods. Our learned agents can also generalize to unseen scenarios while keeping the original performance. The source code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Zengyi-Qin/bcbf</uri> .
Credit and Recognition for Contributions to Data-Sharing Platforms Among Cohort Holders and Platform Developers in Europe: Interview Study

The European Commission is funding projects that aim to establish data-sharing platforms. These platforms are envisioned to enhance and facilitate the international sharing of cohort data. Nevertheless, broad data sharing may be restricted by the lack of adequate recognition for those who share data.The aim of this study is to describe in depth the concerns about acquiring credit for data sharing within epidemiological research.A total of 17 participants linked to European Union-funded data-sharing platforms were recruited for a semistructured interview. Transcripts were analyzed using inductive content analysis.Interviewees argued that data sharing within international projects could challenge authorship guidelines in multiple ways. Some respondents considered that the acquisition of credit for articles with extensive author lists could be problematic in some instances, such as for junior researchers. In addition, universities may be critical of researchers who share data more often than leading research. Some considered that the evaluation system undervalues data generators and specialists. Respondents generally looked favorably upon alternatives to the current evaluation system to potentially ameliorate these issues.The evaluation system might impede data sharing because it mainly focuses on first and last authorship and undervalues the contributor's work. Further movement of crediting models toward contributorship could potentially address this issue. Appropriate crediting mechanisms that are better aligned with the way science ought to be conducted in the future need to be developed.
Compressing and mining social network data

Nowadays, social networking is popular. As such, numerous social networking sites (e.g., Facebook, YouTube, Instagram) are generating very large volumes of social data rapidly. Valuable knowledge and information is embedded into these big social data. As the social network can be very sparse, it is awaiting to be (a) compressed via social network data compression and (b) analyzed and mined via social network analysis and mining. We present in this paper a solution for compressing and mining social networks. It gives an interpretable compressed representation of sparse social network, and discovers interesting patterns from the social network. Results of our evaluation show the effectiveness of our solution in explaining the compression and mining of the sparse social network data.
Holistic Graph Neural Networks based on a global-based attention mechanism

Graph Neural Networks (GNNs) have become increasingly popular due to their impressive capacity to perform classification or regression on high-dimensional graph-structured data. However, standard message passing GNNs typically define nodes embeddings through a recursive neighborhood aggregation process which updates the representation vector of each node with reference to its neighborhood only. In this paper, we propose the Holistic Graph Neural Network (HGNN), a two-fold architecture which introduces a global-based attention mechanism for learning and generating nodes embeddings. The global features we inject, summarize the overall global behavior of the graph in addition to the local semantic and structural information. These global features will make each individual node aware of the global behavior of the graph outside the borders of the local neighborhood. We further propose a variant of the HGNN, we call HGNNα based on a more sophisticated hierarchical global-feature extraction mechanism. We explore diverse global pooling strategies to derive highly expressive global features. We also show that state-of-the-art GNNs can significantly benefit from the addition of the global-based attention introduced. Furthermore, we prove the efficiency of the HGNN model theoretically and adapt it to support graph data which carries edge attributes for example the Molecular datasets from the Open Graph Benchmark. Experiments on Bioinformatics datasets, Social Networks and Molecular datasets demonstrate that our proposed models achieve much better performance than state-of-the-art methods, for instance we achieved improvements of +11% on COLLAB and +13% on IMDB-BINARY datasets.
On computing the similarity of trapezoidal fuzzy sets using an Automated Area Method

This paper provides a new Automated Area Method (AAM) for computing the Jaccard similarity of non-normal trapezoidal fuzzy sets (with triangles and rectangles as special cases), needed, e.g. in XAI for rule-based fuzzy systems, Computing With Words using the Perceptual Computer, Perceptual reasoning, etc. The most intensive part of the Jaccard similarity computation is computing the area under the minimum of two fuzzy sets. The AAM does this very efficiently by initially using figures for the 40 possible cases of the minimum of two non-normal trapezoidal type-1 FSs, and by then providing area formulas for each of them using simple geometry. Flowcharted algorithms are then provided for automatically determining which of the 40 cases one is in, which do not need any figures, and only need the coordinates of the vertices of the two non-normal trapezoidal type-1 FSs. Examples are provided that illustrate these new results, both for type-1 and interval type-2 FSs. The AAM can also be used to compute other similarity measures and to compute the subsethood between fuzzy sets.
A novel pixel’s fractional mean-based image enhancement algorithm for better image splicing detection

Image manipulation has become widely accessible to the masses over the past years due to the sophisticated image editing tools which are readily-available and easy to use. As a result, image forgery has increased such that it has become infeasible to discriminate authentic from tampered images with the naked eye. Image forgery plays a prominent role in the spread of misinformation, which might be criminalized under certain jurisdictions. Image splicing is a common type of image manipulation and constitutes one of the most widespread image tampering methods on the internet. Efforts have been made to tackle the implications of image forgery by developing computer algorithms that can discriminate tampered images, however, more research is needed to keep up with the advancements of image editing tools. Previously, we have explored fractional calculus in other image processing applications. In this study, we propose a novel pixel's fractional mean (PFM) algorithm to enhance images prior to classification for better detection of image splicing forgery based on texture features. The proposed PFM enhances each pixel separately depending on the occurrence number of the pixel's intensity. Two sets of texture algorithms are used to extract essential features from suspected spliced images. These features are then used with the "support vector machine" (SVM) classifier to classify authentic and spliced images. The proposed model demonstrated an accuracy rate of 97% when evaluated with the publicly-available image splicing dataset "CASIA v2.0". With a relatively low dimension feature vector, the proposed model demonstrates high accuracy and efficiency, which corroborate the benefit of using fractional calculus in image processing algorithms.
Fuzzy Logic Based Support Vector Regression (SVR) Model for Software Cost Estimation Using Machine Learning

In this paper, a fuzzy logic (FL) based support vector regression (SVR) model along with COCOMO-II model by simulating machine learning is proposed and introduced. By using the benefits of fuzzy logic like adaption, accuracy in estimation, understandability and so on, can increase the estimation accuracy. Imposing fuzzy logic to any approach could lead to best performance by minimizing inaccuracy of input–output parameters. This proposed model uses fuzzy logic for overcoming the inaccuracy and ambiguity of parameters and provides high-accuracy results. More often, machine learning techniques such as ant colony optimization (ACO), K-Modes, Random forest and basic COCOMO model have been implemented and also comparison conducted in our proposed FL-COCOMO-II model. It shows that our proposed FL-COCOMO-II model provides better performance over the existing models. Therefore, the obtained results offer better accuracy, recall, execution time and runtime of the proposed model over the other existing techniques.
Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Virtual Measurement Model

Random matrices are widely used to estimate the extent of an elliptically contoured object. Usually, it is assumed that the measurements follow a normal distribution, with its standard deviation being proportional to the object’s extent. However, the random matrix approach can filter the center of gravity and the covariance matrix of measurements independently of the measurement model. This work considers the whole chain from data acquisition to the linear Kalman Filter with extension estimation as a reference plant. The input is the (unknown) ground truth (position and extent). The output is the filtered center of gravity and the filtered covariance matrix of the measurement distribution. A virtual measurement model emulates the behavior of the reference plant. The input of the virtual measurement model is adapted using the proposed algorithm until the output parameters of the virtual measurement model match the result of the reference plant. After the adaptation, the input to the virtual measurement model is considered an estimation for position and extent. The main contribution of this paper is the reference model concept and an adaptation algorithm to optimize the input of the virtual measurement model.
Formal Modeling and Verification of a Blockchain-Based Crowdsourcing Consensus Protocol

Crowdsourcing is an effective technique that allows humans to solve complex problems that are hard to accomplish by automated tools. Some significant challenges in crowdsourcing systems include avoiding security attacks, effective trust management, and ensuring the system’s correctness. Blockchain is a promising technology that can be efficiently exploited to address security and trust issues. The consensus protocol is a core component of a blockchain network through which all the blockchain peers achieve an agreement about the state of the distributed ledger. Therefore, its security, trustworthiness, and correctness have vital importance. This work proposes a Secure and Trustworthy Blockchain-based Crowdsourcing (STBC) consensus protocol to address these challenges. Model checking is an effective and automatic technique based on formal methods that is utilized to ensure the correctness of STBC consensus protocol. The proposed consensus protocol’s formal specification is described using Communicating Sequential Programs (CSP#). Safety, fault tolerance, leader trust, and validators’ trust are important properties for a consensus protocol, which are formally specified through Linear Temporal Logic (LTL) to prevent several security attacks, such as blockchain fork, selfish mining, and invalid block insertion. Process Analysis Toolkit (PAT) is utilized for the formal verification of the proposed consensus protocol.
A Novel Effective Framework for Medical Images Secure Storage Using Advanced Cipher Text Algorithm in Cloud Computing

Cloud computing is one of the emerging technologies in present world. Many cloud computing services such as data storage, data usage and other types of services are available. Medical images storage is most widely used in many hospitals because storing of medical images is significant task. Security plays the major role in cloud computing storage, especially for privacy sensitive medical imaging data. Cloud storage gives security for every file available in the relevant cloud server. To access the data from cloud, various encryption and decryption algorithms are available. In this paper, the Advanced Cipher-text Algorithm (ACTA) is introduced to provide extra security needed tools for the privacy sensitive medical images in cloud. Storing efficiently in a secure way medical images is a computationally involved task because of the complexity due to high image resolution. In the herein conducted experimental study the results obtained show the promising performance of the proposed algorithm.
Local feature based self-embedding fragile watermarking scheme for tampered detection and recovery utilizing AMBTC with fuzzy logic

The aim of designing self-embedding fragile watermarking is copy-right protection, tamper localization and image recovery. This paper proposes a self-embedding fragile watermarking scheme for tamper detection and recovery based on local image characteristics using Absolute Moment Block Truncation Coding (AMBTC) and fuzzy logic. The original image is divided into (4×4) non-overlapping blocks. The watermark of each block are generated from 6 MSBs of each pixels of the block. Using similarity matrix, the blocks are categorized into smooth and complex depending on similarity among neighboring pixels. In smooth block, mean is used as recovery data for their high similarity existence whereas complex block uses AMBTC image as it has a compressed representation of an image and preserves the features of the cover image. Recovery data are embedded into 2 LSBs of corresponding mapped block. The efficiency of the proposed scheme is analyzed in terms of Tamper Detection Rate (TDR) and Tamper Recovery Rate (TRR). The performance of the proposed scheme is analyzed against different attacks applied on different regions of the watermarked image. The experiment results show that the imperceptibility and visual quality of the watermarked image as well as recovered image is satisfactorily high even at 50% tampering rate.
Next frame prediction using ConvLSTM

Abstract Intelligent decision-making systems require the potential for forecasting, foreseeing, and reasoning about future events. The issue of video frame prediction has aroused a lot of attention due to its usefulness in many computer vision applications such as autonomous vehicles and robots. Recent deep learning advances have significantly improved video prediction performance. Nevertheless, as top-performing systems attempt to foresee even more future frames, their predictions become increasingly foggy. We developed a method for predicting a future frame based on a series of prior frames that services the Convolutional Long-Short Term Memory (ConvLSTM) model. The input video is segmented into frames, fed to the ConvLSTM model to extract the features and forecast a future frame which can be beneficial in a variety of applications. We have used two metrics to measure the quality of the predicted frame: structural similarity index (SSIM) and perceptual distance, which help in understanding the difference between the actual frame and the predicted frame. The UCF101 data set is used for testing and training in the project. It is a data collection of realistic action videos taken from YouTube with 101 action categories for action detection. The ConvLSTM model is trained and tested for 24 categories from this dataset and a future frame is predicted which yields satisfactory results. We obtained SSIM as 0.95 and perceptual similarity as 24.28 for our system. The suggested work’s results are also compared to those of state-of-the-art approaches, which are shown to be superior.
A Conceptual Framework to Ensure Privacy in Patient Record Management System

Privacy has become an increasingly significant apprehension in today's rapidly changing economy primarily for personal and sensitive user data.The levels of personal data violation are increasing day by day however privacy-preserving frameworks are available.This paper conducted an in-depth analysis of contemporary frameworks to identify the key mechanisms to produce a sophisticated data privacy framework to reduce the rate of data breach particularly for the Patient Record Management System (PRMS).There are several studies available that stated healthcare data privacy, still, complete data protection solution with the application of privacy by design towards patients' health data by ensuring privacy in each layer of the PRMS are quite limited, which is the focus of this study.PRMS manages personal and sensitive data while delivering healthcare services to the patients and as such, have also the potential to carry significant risks to the privacy of their data.A novel conceptual framework with three distinct and sequential phases is suggested in this research, each of which is defined in a distinct section.The first phase is defined as the planning to identify the key limitations of contemporary frameworks so these can be minimized to ensure privacy in each layer of data processing.The second phase incorporates the key components of data privacy to satisfy the efficiency and effectiveness of the proposed framework.Finally, the third phase is the implementation of the selected requirements of the assessment phase to prevent privacy incursion events in PRMS.The complete framework is anticipated to deliver a sophisticated resistance in contradiction to the continuous data breaches in the patients' information domain.
A study of BERT for context-aware neural machine translation

Context-aware neural machine translation (NMT), which targets at translating sentences with contextual information, has attracted much attention recently. A key problem for context-aware NMT is to effectively encode and aggregate the contextual information. BERT (Devlin et al., in: NAACL, 2019) has been proven to be an effective feature extractor in natural language understanding tasks, but it has not been well studied in context-aware NMT. In this work, we conduct a study about leveraging BERT to encode the contextual information for NMT, and explore three commonly used methods to aggregate the contextual features. We conduct experiments on five translation tasks and find that concatenating all contextual sequences as a longer one and then encoding it by BERT obtains the best translation results. Specifically, we achieved state-of-the-art BLEU scores on several widely investigated tasks, including IWSLT’14 German $$\rightarrow$$ English, News Commentary v11 English $$\rightarrow$$ German translation and OpenSubtitle English $$\rightarrow$$ Russian translation.
Haze Removal for a Single Remote Sensing Image Using Low-Rank and Sparse Prior

Due to the influence of atmospheric scattering, the quality of remote sensing images is degraded, which severely limits the utility of remote sensing images. In this article, a novel dehazing algorithm for a single remote sensing image is proposed based on a low-rank and sparse prior (LSP). According to an atmospheric scattering model, the dark channel of a hazy image is decomposed into two parts: the dark channel of direct attenuation with sparseness and the atmospheric veil with low rank. The prior is obtained from the overall decomposition of the image rather than the patches of the image; therefore, the image pixel changes of the local blocks have little influence on the prior. Considering different resolutions of remote sensing images, the calculations of blocks involved in this article are completed by adaptive methods. The principal component pursuit and alternating direction multiplier method (PCP-ADMM) combined with the adaptive threshold shrinkage method are used for low-rank and sparse decomposition, therefore, the coarse estimation of the atmospheric veil is obtained. The guided filter with adaptive radius is used to refine it, and then the accurate atmospheric light is estimated. Finally, using the deformed atmospheric scattering model based on the atmospheric veil and atmospheric light, the haze-free image is restored. Extensive experimental results on publicly available data sets show that the dehazed images have abundant detail, high contrast, and minimal color distortion when using the proposed method, which is competitive with most state-of-the-art technologies.