Reviewerly/exHarmony huggingface dataset:
CHAD: Combinatory Homomorphic Automatic Differentiation

We introduce Combinatory Homomorphic Automatic Differentiation (CHAD), a principled, pure, provably correct define-then-run method for performing forward and reverse mode automatic differentiation (AD) on programming languages with expressive features. It implements AD as a compositional, type-respecting source-code transformation that generates purely functional code. This code transformation is principled in the sense that it is the unique homomorphic (structure preserving) extension to expressive languages of Elliott’s well-known and unambiguous definitions of AD for a first-order functional language. Correctness of the method follows by a (compositional) logical relations argument that shows that the semantics of the syntactic derivative is the usual calculus derivative of the semantics of the original program. In their most elegant formulation, the transformations generate code with linear types. However, the code transformations can be implemented in a standard functional language lacking linear types: While the correctness proof requires tracking of linearity, the actual transformations do not. In fact, even in a standard functional language, we can get all of the type-safety that linear types give us: We can implement all linear types used to type the transformations as abstract types by using a basic module system. In this article, we detail the method when applied to a simple higher-order language for manipulating statically sized arrays. However, we explain how the methodology applies, more generally, to functional languages with other expressive features. Finally, we discuss how the scope of CHAD extends beyond applications in AD to other dynamic program analyses that accumulate data in a commutative monoid.
Multi-view learning based on maximum margin of twin spheres support vector machine

Multi-view learning utilizes information from multiple representations to advance the performance of categorization. Most of the multi-view learning algorithms based on support vector machines seek the separating hyperplanes in different feature spaces, which may be unreasonable in practical application. Besides, most of them are designed to balanced data, which may lead to poor performance. In this work, a novel multi-view learning algorithm based on maximum margin of twin spheres support vector machine (MvMMTSSVM) is introduced. The proposed method follows both maximum margin principle and consensus principle. By following the maximum margin principle, it constructs two homocentric spheres and tries to maximize the margin between the two spheres for each view separately. To realize the consensus principle, the consistency constraints of two views are introduced in the constraint conditions. Therefore, it not only deals with multi-view class-imbalanced data effectively, but also has fast calculation efficiency. To verify the validity and rationlity of our MvMMTSSVM, we do the experiments on 24 binary datasets. Furthermore, we use Friedman test to verify the effectiveness of MvMMTSSVM.
Handwritten MODI Character Recognition Using Transfer Learning with Discriminant Feature Analysis

"MODI lipi" is one of the ancient scripts of Western India. Considerable work has been reported for various other ancient Indian languages except for MODI lipi. Its structural characteristics and non-availability of image database make MODI recognition challenging. The work reported in this paper comprises the creation of an image dataset for MODI handwritten characters and the development of a supervised Transfer Learning (TL)-based classification framework. It makes use of Deep Convolutional Neural Network (DCNN) Alexnet as a pre-trained network to transfer weights to retrain the network. This network is used as a feature extractor to extract features from different layers of the network. A Support Vector Machine (SVM) is trained on activation features to obtain classifier models. These models are investigated further for recognition accuracy and feature analysis. Subjective and objective measures are used to select discriminant deep features. We achieved recognition accuracies of 92.32% and 97.25% for Handwritten MODI character recognition and handwritten Devnagari character recognition, respectively.
Performance analysis on Diversity Mining‐based Proof of Work in bifolded consortium blockchain for Internet of Things consensus

Summary Internet of Things (IoT) networks have become part and parcel of the present life. In the present‐day scenario, IoT networks are distributed, and they generate heterogeneous data to be shared, processed and analyzed. But, the prevailing centralized IoT architecture has numerous security vulnerabilities, specifically in data hosting and transfer. Blockchain, a cryptographically distributed tamper‐proof database when blends with IoT, enables a secure data archival and exchange of peer‐to‐peer (P2P) transactions to be more private. In this work, a multilayered consortium blockchain network that inherits the benefits of private blockchain for its privacy and public blockchain's decentralized governance is launched. The adoption of a semi‐private system in an IoT environment enables regulated data transparency to form novel HIBchain. It can support the heterogeneity of IoT data and has the principle of marking permissions to the public addresses. The out‐turn of an off‐chain layer is the hashed IoT blocks with a consistent transaction volume of 464 bits. The most common techniques are computationally demanding and frequently unfeasible in IoT contexts. We design and analyze the diversity mining‐based proof of work (DM‐PoW) consensus protocol on HIBchain, which yields an average processing time of 1760 transactions per second (TPS) and mining time of 3 minutes.
What makes an idea worth spreading? Language markers of popularity in <scp>TED</scp> talks by academics and other speakers

Abstract TED talks are a popular internet forum where new ideas and research are presented by a wide variety of speakers. In this study, we investigated how the language used in TED talks influenced popularity and viewer ratings. We also investigated the differences in linguistic style and ratings of talks given by academics and non‐academics. The transcripts of 1866 talks were analyzed using the Linguistic Inquiry and Word Count program and eight language variables were correlated with number of views and viewer ratings. We found that talks with more analytic language received fewer views, while a greater use of the pronoun “I,” positive emotion and social words was associated with more views. Talks with these linguistic characteristics received more emotional viewer ratings such as inspiring or courageous. When comparing talks by academics and non‐academics, there was no difference in the overall popularity but viewers rated talks by academics as more fascinating, informative, and persuasive while non‐academics received higher emotional ratings. The implications for understanding social influence processes are discussed.
A fundamental group for digital images

We define a fundamental group for digital images. Namely, we construct a functor from digital images to groups, which closely resembles the ordinary fundamental group from algebraic topology. Our construction differs in several basic ways from previously established versions of a fundamental group in the digital setting. Our development gives a prominent role to subdivision of digital images. We show that our fundamental group is preserved by subdivision.
Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

With the advent of the pandemic (e.g., novel corona virus disease 2019 (COVID-19)), a tremendous amount of data about individuals are collected by the health authorities on daily basis for curbing the disease’s spread. The individuals’ data collection/processing at a massive scale for community well-being with the help of digital solutions (e.g., mobile apps for mobility and proximity analysis, contact tracing through credit card usage history, facial recognition through cameras, and crowd analysis using cellular networks data etc.) raise several privacy concerns. Furthermore, the privacy concerns that are arising mainly due to the fine-grained data collection has hindered the response to tackle this pandemic in many countries. Hence, acquiring/handling individuals data with privacy protection has become a vibrant area of research in these pandemic times. This paper explains the shift in privacy paradigm due to the pandemic (e.g., COVID-19) which involves more and detailed data collection about individuals including locations and demographics. We explain technical factors due to which the people’s privacy is at higher risk in the COVID-19 time. In addition, we discuss privacy concerns in different epidemic control measures (ECMs) (e.g., contact tracing, quarantine monitoring, and symptoms reporting etc.) employed by the health authorities to tackle this disease. Further, we provide an insight on the data management in the ECMs with privacy protection. Finally, the future prospects of the research in this area tacking into account the emerging technologies are discussed. Through this brief article, we aim to provide insights about the vulnerability to user’s privacy in pandemic times, likely privacy issues in different ECMs adopted by most countries around the world, how to preserve user’s privacy effectively in all phases of the ECMs considering relevant data in loop, and conceptual foundations of ECMs to fight with future pandemics in a privacy preserving manner.
Transfer Learning for Brain Tumor Segmentation

Gliomas are the most common malignant brain tumors that are treated with chemoradiotherapy and surgery. Magnetic Resonance Imaging (MRI) is used by radiotherapists to manually segment brain lesions and to observe their development throughout the therapy. The manual image segmentation process is time-consuming and results tend to vary among different human raters. Therefore, there is a substantial demand for automatic image segmentation algorithms that produce a reliable and accurate segmentation of various brain tissue types. Recent advances in deep learning have led to convolutional neural network architectures that excel at various visual recognition tasks. They have been successfully applied to the medical context including medical image segmentation. In particular, fully convolutional networks (FCNs) such as the U-Net produce state-of-the-art results in the automatic segmentation of brain tumors. MRI brain scans are volumetric and exist in various co-registered modalities that serve as input channels for these FCN architectures. Training algorithms for brain tumor segmentation on this complex input requires large amounts of computational resources and is prone to overfitting. In this work, we construct FCNs with pretrained convolutional encoders. We show that we can stabilize the training process this way and achieve an improvement with respect to dice scores and Hausdorff distances. We also test our method on a privately obtained clinical dataset.
An Extensive Review of Charged System Search Algorithm for Engineering Optimization Applications

In recent decades, there has been a growing interest in utilizing different metaheuristic algorithms for optimization purposes. Charged system search algorithms are one of the recently proposed metaheuristic optimization algorithms that are formulated based on some principles of mechanics and physics that mimics the Newtonian and Coulomb laws of mechanics and electrostatics to formulate a search technique. In this book chapter, an extensive review based on the applications of this algorithm in different optimization fields is presented in order to represent the generality of this algorithm. A summary of the modified versions of this metaheuristic and their applications in different optimization areas are also presented alongside the hybrid and multi-objective approaches. For the first time in this book chapter, a comprehensive programming code of this algorithm is also provided. A complete discussion about the new challenges in improving this algorithm or implementing in different optimization problems is provided accordingly.
von Mises–Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning

Recent work has argued that classification losses utilizing softmax cross-entropy are superior not only for fixed-set classification tasks, but also by outperforming losses developed specifically for open-set tasks including few-shot learning and retrieval. Softmax classifiers have been studied using different embedding geometries—Euclidean, hyperbolic, and spherical—and claims have been made about the superiority of one or another, but they have not been systematically compared with careful controls. We conduct an empirical investigation of embedding geometry on soft-max losses for a variety of fixed-set classification and image retrieval tasks. An interesting property observed for the spherical losses lead us to propose a probabilistic classifier based on the von Mises–Fisher distribution, and we show that it is competitive with state-of-the-art methods while producing improved out-of-the-box calibration. We provide guidance regarding the trade-offs between losses and how to choose among them.
Ontology Based Governance for Employee Services

Advances in computers and communications have significantly changed almost every aspect of our daily activity. In this maze of change, governments around the world cannot remain indifferent. Public administration is evolving and taking on a new form through e-government. A large number of organizations have set up websites, establishing an online interface with the citizens and businesses with which it interacts. However, most organizations, especially the decentralized agencies of the ministries and local authorities, do not offer their information electronically despite the fact that they provide many information services that are not integrated with other e-government services. Besides, these services are mainly focused on serving citizens and businesses and less on providing services to employees. In this paper, we describe the process of developing an ontology to support the administrative procedures of decentralized government organizations. Finally, we describe the development of an e-government portal that provides employees services that are processed online, using the above ontology for modeling and data management.
Combined adaptive robust Kalman filter algorithm

Abstract The precise positioning of dynamic and static objects such as vehicles and pedestrians is a key technology. A global navigation satellite system signal is the primary signal source required to achieve precise positioning, and the optimal estimation method used in precise positioning is Kalman filtering (KF). Standard KF can only achieve optimal estimation results under the conditions that the mathematical model has been determined and the noise characteristics are known. However, when there are measurement outliers and prior noise information deviations, the accuracy of KF cannot be guaranteed. To effectively solve the above problems, a combined adaptive robust Kalman filter (CARKF) algorithm is proposed. First, the influence of measurement outliers on KF accuracy is resisted using the robust Kalman filter method. Then, the influence of an anomalous innovation on its autocorrelation sequence and cross-correlation sequence is resisted by the robust estimator for correlated observations based on the IGGIII method, to ensure the correct estimation of the correlation function sequence of the filtered innovation statistics. Finally, the autocovariance least-squares method is used to eliminate the coupling effect between noise covariances and accurately estimate the unknown noise covariance information. Moreover, an iterative strategy is adopted to eliminate the effect of errors caused by prior noise covariance deviations. The results of the three experiments show that the CARKF method can not only effectively resist the influence of measurement outliers on filtering accuracy and noise covariance valuation but also overcome the influence of errors caused by prior noise deviations and accurately simultaneously estimate two kinds of unknown noise covariance information.
A guidance to implement or reinforce a DevOps approach in organizations: A case study

Abstract In recent years, stakeholders expect the delivery of new features almost every day to give fast feedback about changes or issues in software projects. To achieve this, they have opted to try new practices such as those from DevOps to improve their processes. Organizations such as IBM, Facebook, Amazon, and so on are implementing a DevOps culture. However, for small organizations, which are a large majority in the software development market, it is complicated to adopt DevOps because there is no guide to follow. Organizations have experienced issues trying to implement a DevOps approach, mainly for reasons such as a lack of process, guidance, or uncoordinated activities among organizations. Moreover, some organizations have a wrong understanding of DevOps: as just a set of automation tools to achieve a continuous release. This paper presents a guide to implement or reinforce a DevOps approach. Besides, the results of a case study of implementing the guide in a small organization are included. These results have shown that the guide helped the team: to identify its gaps toward the implementation of a DevOps approach; to have a better understanding of DevOps; and to facilitate the DevOps implementation providing a set of tasks, roles, and metrics.
Instance Segmentation Based on Deep Convolutional Neural Networks and Transfer Learning for Unconstrained Psoriasis Skin Images

In this paper, an efficient instance segmentation scheme based on deep convolutional neural networks is proposed to deal with unconstrained psoriasis images for computer-aided diagnosis. To achieve instance segmentation, the You Only Look At CoefficienTs (YOLACT) network composed of backbone, feature pyramid network (FPN), Protonet, and prediction head is used to deal with psoriasis images. The backbone network is used to extract feature maps from an image, and FPN is designed to generate multiscale feature maps for effectively classifying and localizing objects with multiple sizes. The prediction head is used to predict the classification information, bounding box information, and mask coefficients of objects. Some prototypes generated by Protonet are combined with mask coefficients to estimate the pixel-level shapes for objects. To achieve instance segmentation for unconstrained psoriasis images, YOLACT++ with a pretrained model is retrained via transfer learning. To evaluate the performance of the proposed scheme, unconstrained psoriasis images with different severity levels are collected for testing. As for subjective testing, the psoriasis regions and normal skin areas can be located and classified well. The four performance indices of the proposed scheme were higher than 93% after cross validation. About object localization, the Mean Average Precision (mAP) rates of the proposed scheme were at least 85.9% after cross validation. As for efficiency, the frames per second (FPS) rate of the proposed scheme reached up to 15. In addition, the F1_score and the execution speed of the proposed scheme were higher than those of the Mask Region-Based Convolutional Neural Networks (R-CNN)-based method. These results show that the proposed scheme based on YOLACT++ can not only detect psoriasis regions but also distinguish psoriasis pixels from background and normal skin pixels well. Furthermore, the proposed instance segmentation scheme outperforms the Mask R-CNN-based method for unconstrained psoriasis images.
Melodie

Despite the promise of the maker movement as empowering individuals and democratizing design, people with disabilities still face many barriers to participation. Recent work has highlighted the inaccessible nature of making and introduced more accessible maker technologies, practices, and workspaces. One less explored area of accessible making involves supporting more traditional forms of craftwork, such as weaving and fiber arts. The present study reports an analysis of existing practices at a weaving studio within a residential community for people with vision impairments and explores the creation of an audio-enhanced loom to support this practice. Our iterative design process began with 60 hours of field observations at the weaving studio, complemented by 15 interviews with residents and instructors at the community. These insights informed the design of Melodie, an interactive floor loom that senses and provides audio feedback during weaving. Our design exploration of Melodie revealed four scenarios of use among this community: promoting learning among novice weavers, raising awareness of system state, enhancing the aesthetics of weaving, and supporting artistic performance. We identify recommendations for designing audio-enhanced technologies that promote accessible crafting and reflect on the role of technology in predominantly manual craftwork.
Developing automated methods to estimate spectrally resolved direct normal irradiance for solar energy applications

We describe four schemes designed to estimate spectrally resolved direct normal irradiance (DNI) for multi-junction concentrator photovoltaic systems applications. The schemes have increasing levels of complexity in terms of aerosol and circumsolar irradiance (CSI) treatment, ranging from a climatological aerosol classification with no account of CSI, to an approach which includes explicit aerosol typing and type dependent CSI contribution. When tested against ground-based broadband and spectral measurements at five sites spanning a range of aerosol conditions, the most sophisticated scheme yields an average bias of +0.068%, well within photometer calibration uncertainties. The average spread of error is 2.5%. These statistics are markedly better than the climatological approach, which carries an average bias of −1.76% and a spread of 4%. They also improve on an intermediate approach which uses Angström exponents to estimate the spectral variation in aerosol optical depth across the solar energy relevant wavelength domain. This approach results in systematic under and over-estimations of DNI at short and long wavelengths respectively. Incorporating spectral CSI particularly benefits sites which experience a significant amount of coarse aerosol. All approaches we describe use freely available reanalyses and software tools, and can be easily applied to alternative aerosol measurements, including those from satellite.