Reviewerly/exHarmony huggingface dataset:
Interview with Heyward Ehrlich (February 2, 2022)

Meeting Report| June 01 2022 Interview with Heyward Ehrlich (February 2, 2022) Barbara Cantalupo Barbara Cantalupo Search for other works by this author on: This Site Google The Edgar Allan Poe Review (2022) 23 (1): 117–126. https://doi.org/10.5325/edgallpoerev.23.1.0117 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Barbara Cantalupo; Interview with Heyward Ehrlich (February 2, 2022). The Edgar Allan Poe Review 1 June 2022; 23 (1): 117–126. doi: https://doi.org/10.5325/edgallpoerev.23.1.0117 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectivePenn State University PressThe Edgar Allan Poe Review Search Advanced Search You do not currently have access to this content.
Infinite families of $ t $-designs and strongly regular graphs from punctured codes

<p style='text-indent:20px;'>The puncturing technique is sometimes efficient in constructing projective codes from original codes which are not projective. In this paper, several families of projective linear codes punctured from reducible cyclic codes, special linear codes or irreducible cyclic codes are investigated. The parameters and weight enumerators of the punctured codes and their duals are explicitly determined. Some of the codes are optimal and some of the codes are self-orthogonal which can be used to construct quantum codes. Several infinite families of combinatorial <inline-formula><tex-math id="M2">\begin{document}$ 2 $\end{document}</tex-math></inline-formula>-designs and <inline-formula><tex-math id="M3">\begin{document}$ 3 $\end{document}</tex-math></inline-formula>-designs including some families of Steiner systems are constructed from the punctured codes and their duals. Besides, infinite families of strongly regular graphs are also derived from some families of two-weight projective codes.
Better Programming Principles for Efficient Data Science

The goal of this chapter is to introduce you to the concepts of certain programming styles and habits that play an essential part in developing efficient data science (DS) and machine learning (ML) systems and pipelines. I will illustrate the concepts through brief examples (or pseudo-codes wherever applicable) and talk about how to measure or track inefficiency. This chapter has the central objective of instilling a sense of probing into your mind, which you can use anywhere and everywhere in their codebase, to dig deep and probe the efficiency of your implementations and compare among alternative solutions.
The Potential Application of Augmented Reality (AR) for Conserving the British Pillbox in Kelantan

The British pillbox in Kelantan has become an attraction for tourists. The pillbox used to be the defender for the British in Malaysia, considering an attack on the Japanese who come from various angles. The pillbox still exists around the residential area. The passage of time has made the pillbox suffer various damages and deteriorations. This pillbox cannot be conserved physically. There are structural differences such as the number of blocks, size, and height of these monuments which may have certain functions and reasons too. This research aims at two locations of pillbox which is located on Pantai Chap, Bachok and Pulau Kundur, Kota Bharu. This research aims to identify the potential of Augmented Reality for conserving the British pillbox in Kelantan. It is also to determine the appropriate Augmented Reality to use in conserving the British pillbox in Kelantan. Detailed research was carried out to obtain evidence such as photographs, structural details, and pillbox location. The data has been applied to Unite AR, one of Augmented Reality (AR) applications to conserve this British pillboxes’ heritage monument. Through this research, the AR was created to expose the real structures of two British pillboxes located in Kg. Pantai Chap and Kg. Pulau Kundur. This research intended to extend the uses of Augmented Reality (AR) in conservation since many museums use this technology to conserve artifacts and monuments and attract people to exhibitions. By using this conservation method, the monuments will remain and new generations will appreciate more about history.
On the Technical Debt Prioritization and Cost Estimation with SonarQube Tool

Commonly, software developers are faced with situations to compromise internal quality to achieve short term goals, e.g. time-to-market. In software engineering, such compromises are described with Technical Debt (TD) concept. TD implies cost of additional rework—usually expressed as effort—and when the code is compromised, it is called code debt. One of the most popular tools for identifying TD items and estimating effort for solving them is SonarQube. However, there is still a need for empirical validations of remediation times that SonarQube estimates. The objective of this research is to validate the usefulness of the tool for novice developers in terms of understanding SonarQube’s categorization of criticality levels of TD issues and accuracy of estimated remediation times. We designed and conducted an empirical study with 185 students in a context of university software engineering courses in Finland and Serbia. The estimates provided by SonarQube are more pessimistic, i.e. only in 3% of cases the actual fixing time was higher that the estimated one. Our results also indicate that participants’ perception of the criticality levels of the TD items is misaligned with SonarQube’s classification and prioritization.
Comic Contents Retrieval Support Interface Using Speech Frequencies of Characters

Over the past few decades, reading comics has become a globally accepted and appreciated form of entertainment. Although certain comics are easy to be found at bookstores and search engines, it is not guaranteed that readers can find the exact chapter and episode they desire. If the reader happens to find some unread chapters or episodes, It can end up being a spoiler. In this study, the authors do something by hypothesizing that readers can recall the content of comics by looking at the characters’ speech frequencies in the already read part. This paper proposes an interface to support the retrieval of comic content by visualizing the speech frequencies of characters in each volume using a stacked area graph. Through experiments, the authors confirmed that the proposed interface could support the retrieval of comic content.
Hello World

In this chapter, we discuss and build the various projects needed for a simple “Hello, World” program in Orleans. While we could build a Hello World in a single C# file, it is important to understand how to separate concerns and improve maintainability by organizing Orleans code into separate projects.
A Hybrid Cryptographic Algorithm for securing medical data in Cloud Computing Environment

In the past few years, the researchers focused on cloud computing security, because of medical data usage and its importance. Therefore, data security on medical data is an important issue, while storing it on clouds. Decentralized protection safeguarding prescient displaying enables numerous organisations to learn about a more generalize on medical services or genomic information by sharing the halfway prepared models rather than patient-level information, while evading dangers, such as single-purpose of control. One of the most important aspects of human life has become restorative care, which has led to a dramatic increase in therapeutic data. Although these advancements pose serious security risks and concerns about the movement of information, they also raise serious concerns about data tracking and recording. Even if the patient&#x2019;s life is in jeopardy, these security and protection issues of medical information could result from a delay in treatment. A high level of data transit and storage security can be achieved through the use of cryptography. Traditional symmetric and asymmetric have various drawbacks in terms of their design. In order to ensure the privacy and data security at high level, the research work implemented the new hybrid technique. Elliptic curve cryptography (ECC) and the SHA-256 Cryptographic Hash Algorithm (SHA-256) are being combined in this article to create a hybrid algorithm. Proposed methods for patient data security and confidentiality have been tested and the results proves that it outperforms existing methods. To overcome the drawbacks of both symmetric and asymmetric cryptography, hybrid cryptography is employed.
Automatic Monitoring Technology of Business English Document Translation Equipment Based on Logistic Model

Business English translation requires translators to have rich knowledge of business theory and business practice. In order to improve the quality of translation, translators must have their own conditions. With the continuous development of science and technology, the translation of business English documents has attracted more and more attention. Business English literature contains a large number of professional terms. The translation effect of terms affects the translation quality of business English literature to a great extent, and terms carry the core knowledge in specific fields. Therefore, with the development of information globalization and equipment translation technology, the research of automatic term translation technology has become a hot issue in Business English literature translation. Therefore, this paper studies the automatic monitoring technology of business English document translation equipment based on logistic model.
Methodology to evaluate the impact of learning with the use of physical modeling of structural prototypes

The education of architecture students requires theoretical and practical training in all subjects corresponding to the curriculum. This research shows the methodology to evaluate the impact of the learning of architecture students with the use of physical modeling of structural prototypes, with which it is possible to integrate the conceptual part with the practice. The research method is quantitative quasi-experimental, which must be used on a sample of students, from the Faculty of Architecture and Urbanism of the University of Guayaquil, who must undergo an entry assessment (beginning of the semester), then experiment with the physical modeling of the structural prototypes and, finally, an exit assessment (end of the semester). The results will demonstrate the academic performance after experimenting with the prototypes; the impact on the integration of cognitive processes, the development of creativity, research and innovation.
A Pre-Image Representer Theorem in Machine Learning

The representer theorem is at the core of machine learning, popularized by kernel-based methods and recently extended to deep neural networks. While the representer theorem establishes the model in the space where data are embedded (e.g. feature or latent space), it is often necessary to compute its pre-image in the input space. As the inverse map does not exist in general and the embedding is nonlinear and implicit when using kernel functions, estimating the pre-image is a hard optimization problem. In this paper, we establish a representer theorem for the pre-image problem, by first considering the general class of differentiable maps and then refining it for the wide classes of radial kernels and projective kernels. Moreover, we provide a sufficient condition for the pre-image to lie in the convex hull of the training dataset. These theoretical results lead to a general form of the fixed-point iteration to solve the pre-image problem.
Ideas for Spotify Customization: Enhancing the User’s Experience

The Spotify music streaming app is a popular digital service that allows its users to listen to music just about anywhere. This study explores Spotify users’ experiences searching for music content either independently or via Spotify friend profiles. Findings from observations and interviews demonstrate significant frustrations surrounding the Spotify recommendation services that are privileged throughout the application, as well as difficulties finding friend profiles when searching for content. This study argues for more immediate customization enhancements for users who do not want Spotify’s recommended content burying the music they seek, and provides two prototype views demonstrating design layouts that enhance user control. Prototypes reorient artist discography for each artist page, with the option to toggle Spotify recommendations on and off, as well as improving content search result tags by redesigning these as a more noticeable dropdown menu filter feature. Both prototypes seek to address several of the frustrations study participants expressed when engaging with the Spotify music app.
Violation of Human Rights during COVID-19 and the Role of Indian Judiciary

In the past three years, India has been facing its second biggest challenge after COVID. That is of rampant violation of human rights. With the onset of pandemic, the number of cases filed by marginalised groups for the protection of their human rights increased many-folds. Therefore, it becomes imperative to study in detail the issue of violations and the response of judiciary. In the first part, the researcher examines the various types of violations reported. Violation of citizens right to freedom of speech and expression, privacy, education, access to social services, and equal treatment have been considered. In the next part, intervention by courts has been critically analysed. Then in the end, the researcher discusses the concerns that surfaced while investigation and offers some remedial measures to deal with them.
Image Departmentalizing Using Image Picture Element Comparison Technique

Image gives the accurate meaning than text. Extracting the similar content from the stored data set, image mining plays an important role. Extracting the specific image one of the complex process for many users. Capturing and storing the image done by any unprofessional users because of the development technology. Retrieve the correct information one of the key challenging for many users. To avoid this complexing operation today, most of the contents are properly categorized. Technology supports the users input the needed information either in terms of charter or image. Proposed technique user creates a categorization for image data files for easy and fast access of needed informations. Experimental outputs verified that proposed technique reduces the transmission traffic and reduced searching time.
Image Compression Scheme Based on Histogram Equalization and Convolution Neural Network

Image compression techniques are advancing day by day. Image data’s growth rate is exponentially increasing for the increase of image capture devices, far beyond the compression ratio’s improvement. It recognizes the increasing challenges of research on improving image compression within traditional image compression techniques. The evolution of CNN methodology introduced the image compression scheme. CNN has accelerated neural network research in the present time and has reached excellent success in image computation domains. CNN has provided suitable solutions for image compression. This article proposes an image compression scheme based on histogram equalization (HE) and CNN. We have implemented two image compression schemes. The first scheme is the standard image compression technique using a convolution neural network model (IC-CNN). The second scheme is the proposed image compression model, i.e., histogram equalization (HE) and the CNN-based image compression technique (IC-HE-CNN). The IC-HE-CNN model aims to improve redundancy. Using the Python platform, these two models have been experimented with the high-resolution color images from the ImageNet dataset. We have computed the Bits per pixel (BPP), Peak Signal to Noise Ratio (PSNR), and MS-SSIM basic performance measurement parameters to evaluate image quality and compression efficiency. The experimental results show that the IC-HE-CNN model has achieved a lower BPP value, 0.2049, than the existing standard IC-CNN image compression model and the other four image compression models based on CNN. Also, obtained results indicate that the proposed model has achieved the required level of PSNR and MS-SSIM values.
Image Classification in Breast Histopathology Using Transfer and Ensemble Learning

Breast cancer has the highest prevalence in women globally. The classification of breast cancer histopathological images has always been a hot spot of clinical concern. In Computer-Aided Diagnosis (CAD), traditional models mostly use a single network to extract features, which has significant limitations. Besides, many networks are trained and optimized on patient-level datasets, ignoring the lower-level labels. This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant breast histopathological images. First, the BreakHis dataset is randomly divided into training, validation and test sets. Then, data augmentation techniques are used to balance the number of benign and malignant samples. Thirdly, VGG-16, Xception, Resnet-50, Densenet-201 are selected as the base classifiers resulting from their complementarity. Finally, with accuracy as the ensemble weight, an accuracy of 98.90% is achieved. In order to verify the capabilities of our method, the latest Transformer and Multilayer Perception (MLP) models have been experimentally compared on the same dataset. Our model wins with a 5–20% advantage, emphasizing the ensemble model’s far-reaching significance in classification tasks.
Load Balancing Algorithms in Cloud Computing Environment—An Effective Survey

In recent years, the usage of Internet services and the number of users accessing the cloud systems are increased tremendously since the cloud offers enormous services to users and allows them to access its services from anywhere at any time in a flexible manner. Cloud computing is an emerging technology which has high performance and high throughput systems that can handle multiple users requests simultaneously. However, handling multiple user requests is a major challenge as number of requests increased day by day. It is very difficult for a server to manage all these users request at one time. Sometimes it may result in system breakdown and overloading of servers which causes load unbalancing. Load balancing is a technique in cloud computing that solves the problem of load unbalancing by evenly distributes the user’s request among multiple servers in an optimized way. In this paper, we present an overview of various load balancing algorithms proposed by several authors in recent years with respect to different load balancing metrics and tools used.
Analysis and Performance of JADE on Interoperability Issues Between Two Platform Languages

There are a large number of toolkits and frameworks for multi-agent systems available on the market. These toolkit and framework help the researchers to build an architecture that works on interoperability issues of Web services on different software languages. After studying numerous multi-agent tools, we observed that JADE is a suitable multi-agent software tool that acts as a bridge between inter-platform languages and works efficiently on a distributed network. This paper shows the results and analysis of different interoperability issues of Web service between the two languages, Java and .NET, and proves the quality and maturity of JADE. The analysis focuses on interoperability issues like precision issues of data types, array with null values, unsigned numbers, complex data structure, and date–time formats between Java and .NET, and how JADE acts as middleware, built the agent handler, and resolves the Web service interoperability issues effectively.
Generalized context-dependent graph-theoretic model of folklore and literary texts

One of the problems of automatic text processing is their attribution. This term is understood as the establishment of the attributes of a text work (determination of authorship, time of creation, place of recording, etc.). The article presents a generalized context-dependent graph-theoretic model designed for the analysis of folklore and literary texts. The minimal structural unit of the model (primitive) is a word. Sets of words are combined into vertices, and the same word can be related to different vertices. Edges and graph substructures reflect the lexical, syntactic and semantic links of the text. The characteristics of the model are its fuzziness, hierarchy and temporality. As examples, a hierarchical graph-theoretical model of components (on the example of literary works by A. S. Pushkin), a temporal graph-theoretic model of a fairy tale plot (on the example of Russian fairy tales by A. M. Afanasyev) and a fuzzy graph-theoretic model of «strong» connections of grammatical classes (on the example of anonymous articles from the pre-revolutionary magazines «Time», «Epoch» and the weekly «Citizen», edited by F. M. Dostoevsky). The model is built in such a way that it can be further explored using artificial intelligence methods (for example, decision trees or neural networks). For this purpose, a format for storing such data was implemented in the information system «Folklore», as well as procedures for entering, editing and analyzing texts and their graph-theoretic models.
Optimizing Performance of Image Processing Algorithms on GPUs

Abstract The application of machine learning algorithms in the field of power grid improves the service level of power enterprises and promotes the development of power grid. NVIDIA Volta and Turing GPUs powered by Tensor Cores can accelerate training and learning performance for these algorithms. With Tensor Cores enabled, FP32 and FP16 mixed precision matrix multiplication dramatically accelerates the throughput and reduces AI training times. In order to explore the cause of this phenomenon, we choose a convolutional neural network (CNN), which is widely used in computer vision, as an example and show the performance characteristics with tensor core on general matrix multiplications and convolution calculations as benchmark. Building a CNN based on cuDNN and TensorFlow, we analyze the performance of CNN from various aspects and optimize performance of it by changing the shape of convolution kernel and using texture memory, etc. The experimental results prove the effectiveness of our methods.
Machine-Assisted Proofs for Institutions in Coq

The theory of institutions provides an abstract mathematical framework for specifying logical systems and their semantic relationships. Institutions are based on category theory and have deep roots in a well-developed branch of algebraic specification. However, there are no machine-assisted proofs of correctness for institution-theoretic constructions—chiefly satisfaction conditions for institutions and their (co)morphisms—making them difficult to incorporate into mainstream formal methods. This paper therefore provides the details of our approach to formalizing a fragment of the theory of institutions in the Coq proof assistant. We instantiate this framework with the institutions $$ FOPEQ $$ for first-order predicate logic and $$ EVT $$ for the Event-B specification language, both of which will serve as an illustration and evaluation of the overall approach.