Reviewerly/exHarmony huggingface dataset:
Discrete Optimization: A Quantum Revolution (Part II)

In part I, we introduce the basics of quantum computing and use Grover's algorithm as a subroutine in a binary search procedure that can solve any discrete optimization problem. In this part, we try to improve the performance of this binary search procedure. For this purpose, we first discuss two well-known quantum algorithms: the quantum counting algorithm of Brassard et al. (1998) and the nested quantum search algorithm of Cerf et al. (2000). We show that both algorithms are dominated by a classical implementation of a nested procedure that uses Grover's algorithm to find (partial) solutions to (nested) problems. We propose two such procedures, and use them to solve 66000 instances of the binary knapsack problem. Our results show that both procedures can match the performance of the best classical algorithms. Whereas the first procedure (a hybrid branch-and-bound algorithm) can exploit the structure of the problem, the second procedure (a random-ascend algorithm) can be used to solve discrete optimization problems that have no clear structure or that are difficult to solve using traditional methods. After improving and generalizing these procedures, we show that they can solve any discrete optimization problem using at most O(╖2^(0.5nb)) operations, where ╖ is the number of operations required to evaluate a solution, n is the number of decision variables, and b is the number of discrete values that can be assigned to each decision variable. In addition, we show that both procedures can also be used as a heurstic to find (near-)optimal solutions using less than O(╖2^(0.5nb)) operations. As such, the procedures are the new state-of-the-art in quantum computing, and can be seen as general-purpose solvers that may one day compete with commercial off-the-shelf solvers such as CPLEX and GUROBI.
Modeling Cyber Loss Severity Using a Spliced Regression Distribution with Mixture Components

Cyber losses in terms of number of records breached under cyber incidents commonly feature a significant portion of zeros, specific characteristics of mid-range losses and large losses, which make it hard to model the whole range of the losses using a standard loss distribution.We tackle this modeling problem by proposing a three-component spliced regression model that can simultaneously model zeros, moderate and large losses and consider heterogeneous effects in mixture components.To apply our proposed model to Privacy Right Clearinghouse (PRC) data breach chronology, we segment geographical groups using unsupervised cluster analysis, and utilize a covariate-dependent probability to model zero losses, finite mixture distributions for moderate body and an extreme value distribution for large losses capturing the heavy-tailed nature of the loss data.Parameters and coefficients are estimated using the Expectation-Maximization (EM) algorithm.Combining with our frequency model (generalized linear mixed model) for data breaches, aggregate loss distributions are investigated and applications on cyber insurance pricing and risk management are discussed.
Enabling Notification Management Platform by AWS (Amazon Web Services) Pinpoint

Major SAAS (Software-as-a-service) Notification management services currently available in the industry have limited business capability of modification of rules and triggers. Also, cconsiderable turnaround time in releasing new features prevents connecting e-Commerce platforms from being agile. One of the key reasons is these services are incompatible with the business of the Indian Subcontinent. Again, high-resolution time for fixing an issue creates a bottleneck in supportability and maintainability. Limited visibility of the performance metrics (confined to daily reports only) also prevents to measure the success of the platform. This paper enables marketplace e-Commerce platform to build a homegrown cloud-enabled platform to define, modify and manage communication triggers for all channels i.e., SMS (Short Message Service), Email, WhatsApp, and Push Notifications while business users would have end to end control from editing of templates (used in each of these channels) to track all metrics relevant to customers transaction-related communications. All independent building blocks of the solution API Gateway (for request routing), Amazon SQS (for request queuing), AWS Lambda (for request orchestration), DynamoDB (for rule management), AWS Pinpoint (for notification triggering), AWS Cloud Formation (for change control management) and Stripo (for template management) are loosely coupled and changes can be configured and promoted to Production independently. This robust to-be platform is planned to be built as a service keeping Indian business aspects in mind. Also, this cloud platform can be extended to run massive campaigns based on customer segmentation.
How Can the Indigenous Bushfood Industry Benefit From Distributed Ledger Technology? – Emerging Application Potentials and Considerations

Emerging distributed ledger technologies (DLTs), including blockchain, have found increasing applications across various industries over the last decade. Prior studies have highlighted numerous opportunities for embracing DLTs across industries and explored their potentials to optimise and even transform industries. This study explores in which ways the indigenous bushfood industry – a niche but an expanding industry – with national and international interest – can benefit from emerging DLTs. Emerging application potentials include the use of DLTs as (a) an institutional infrastructure for the protection of indigenous bush food IP and cultural knowledge; (b) a governance mechanism to ensure flow-on benefits of greater indigenous business ownership; (c) a virtual intermediary to connect indigenous producers with customers; (d) a shared communication solution for supply chain transparency and collaboration; (e) a community-based database for bushfood bush plans and nutrition facts; (f) a storyteller for the provenance of bushfood that contains native ingredients and (g) an open infrastructure for indigenous bushfood business innovation. Moving forward from application areas, considerations about selecting fit-for-purpose DLTs for specific contexts are discussed to offer a strategic thinking in the adoption of DLTs within the indigenous bushfood industry.
Zero Update Encryption Adjustment on Encrypted Database Queries

Multilayered encryption of data is used in the popular approaches to querying encrypted databases, such as [34]. The encryption level of a particular data element can be adjusted to provide a controllable leak of the information about data required for a query execution. Zero Update (ZU) Encryption Adjustment is part of broader Release-Aware Encryption Adjustment (RAEA) schema and seeks to avoid unnecessary data leakage into data storage and reduce numerous communications rounds for multiple query executions. In this paper, we examine Zero Update Encryption Adjustment [11] on the client side. We investigate ZU’s performance by constructing specific queries and analysing performance factors such as increasing the number of expressions conditions in a query or documents matching the results as well as utilising indexes. In addition, we evaluated the ZU using a variety of database models. The performance of ZU is compared using three different databases: MongoDB, OrientDB, and MySQL. The results demonstrate the efficiency of Zero Update Encryption Adjustment, which allows for efficient querying over encrypted data on the client-side while maintaining security and performance trade-offs.
Improving Data Security and Privacy for Ontology Based Data Access

Data integration is the process of combining data from multiple disparate source systems to provide users a unified view of data. Nevertheless, integrating and transforming data from various sources are significant challenges. Also, providing the semantics in data representation is an important issue to perform effective data analysis and visualization. In this context, ontology based approaches are used to provide an effective and flexible combination of data from multiple heterogeneous sources. Ontologies support more expressive representations and consistency for the data integration process. Ontology Based Data Access (OBDA) allows to map an ontology to a data source in order to query heterogeneous data more easier. Hence, data integration and data access are ensured with this mapping. As a result, large amounts of data can be stored more efficiently, more powerful queries can be written, and management of complex information systems can be performed more effectively. However, security of complex information systems should also be considered besides these advantages of OBDA. Ontology Based Access Control (OBAC) enables the enforcement of an access control mechanism through the utilization of Semantic Web technologies. Thus, only authorized entities can access and modify data. In this study, the OBAC and OBDA approaches are integrated. The proposed Semantic Web based solution’s goal is to ensure data security and preserve privacy while providing an efficient processing of data from different heterogeneous sources.
Reporting in large-scale agile organizations: insights and recommendations from a case study in software development

Abstract Application of agile software development methodologies in large-scale organizations is becoming increasingly common. However, working with multiple teams and on multiple products at the same time yields higher coordination and communication efforts compared to single-team settings for which agile methodologies have been designed originally. With the introduction of agile methodologies at scale also comes the need to be able to report progress and performance not only of individual teams but also on higher aggregation of products and portfolios. Due to faster iterations, production of intermediate work results, increased autonomy of teams, and other novel characteristics, agile methodologies are challenging existing reporting approaches in large organizations. Based on 23 interviews with 17 practitioners from a large German car manufacturing company, this case study investigates challenges with reporting in large-scale agile settings. Further, based on insights from the case study, recommendations are derived. We find that combining reporting and agile methodologies in large-scale settings is indeed challenging in practice. Our research contributes to the understanding of these challenges, and points out opportunities for future research to improve reporting in large-scale agile organizations by goal-setting and automation.
Understanding transfer learning for chest radiograph clinical report generation with modified transformer architectures

The image captioning task is increasingly prevalent in artificial intelligence applications for medicine. One important application is clinical report generation from chest radiographs. The clinical writing of unstructured reports is time consuming and error-prone. An automated system would improve standardization, error reduction, time consumption, and medical accessibility. In this paper we demonstrate the importance of domain specific pre-training and propose a modified transformer architecture for the medical image captioning task. To accomplish this, we train a series of modified transformers to generate clinical reports from chest radiograph image input. These modified transformers include: a meshed-memory augmented transformer architecture with visual extractor using ImageNet pre-trained weights, a meshed-memory augmented transformer architecture with visual extractor using CheXpert pre-trained weights, and a meshed-memory augmented transformer whose encoder is passed the concatenated embeddings using both ImageNet pre-trained weights and CheXpert pre-trained weights. We use BLEU(1-4), ROUGE-L, CIDEr, and the clinical CheXbert F1 scores to validate our models and demonstrate competitive scores with state of the art models. We provide evidence that ImageNet pre-training is ill-suited for the medical image captioning task, especially for less frequent conditions (e.g.: enlarged cardiomediastinum, lung lesion, pneumothorax). Furthermore, we demonstrate that the double feature model improves performance for specific medical conditions (edema, consolidation, pneumothorax, support devices) and overall CheXbert F1 score, and should be further developed in future work. Such a double feature model, including both ImageNet pre-training as well as domain specific pre-training, could be used in a wide range of image captioning models in medicine.
Application of mobile sensors based on deep neural networks in sports psychological detection and prevention

This article proposes a low-power, low-cost, programmable neural network processor for IoT applications. Aiming at the human inertial motion capture system, a human motion sensor detection method based on long- and short-term memory network is proposed. It provides a practical method for studying human motion detection based on mobile sensors and portable devices. Based on the research of human movement sensor detection method based on long and short-term memory network, this paper analyzes the current research status of sports psychological fatigue detection through the production mechanism, external performance, evidence and entertainment measures of sports psychological fatigue, in order to obtain a better solution. Quickly eliminate the mental fatigue of athletes. At the same time, improve and improve the physical and psychological functions of athletes of different qualities, and further improve sports performance. So far, the theoretical definition of sports mental fatigue and the operational definition of sports mental fatigue are still the center of discussion and research by sports psychologists. This article will also elaborate on the development of the most important concepts describing sports fatigue at home and abroad, using depth Neural network and mobile sensor technology have carried out research and exploration on the mental recognition of athletes in sports.
A Study of Validity and Satisfaction of Tele-assessment

Objectives: This study aims to examine the validity of two types of remote language screening tests (indirect questionnaire type vs direct observational type), and differences in performance of children as well as satisfaction of their mothers between these two types of remote language screening test. Methods: In total 40 children without any language deficits aged 3 to 6 and their mothers participated in this study. For statistical analysis, a Pearson correlation was conducted between the remote language screening tests (indirect questionnaire type and direct observation type); a PRES (Preschool Receptive-Expressive Language Scale) to find out the concurrent validity, and a paired t-test was conducted to find out if there are significant differences in the performance of the children and satisfaction of their mothers between the two types of screening tests. Additionally as a qualitative analysis, mothers’ preference among two types of the test was asked using an open ended survey. Results: The two types of tests (indirect, direct) showed strong positive correlation with one another. Regarding concurrent validity, both test types showed strong positive correlation with a standardized language test as well. There was no significant difference between the two types in both the performance of the children and the satisfaction of mothers as well. However, 85% percent of mothers preferred the direct observation type citing better accuracy as their reasoning. Conclusion: It is hoped that remote language screening tests can contribute to the implementation of non-face-to-face screening in clinical settings since it is highly validated. Furthermore both two types of tests can be used and complement each other.
Matrices as a diagonal quadratic form over rings of integers of certain quadratic number fields

Let O denote the ring of integers of a quadratic field Q(−7). In 2022, Murtuza and Garge [Murtuza N, Garge A. Universality of certain diagonal quadratic forms for matrices over a ring of integers, Indian Journal of Pure and Applied Mathematics, Published online; December 2022.] gave a necessary and sufficient condition for a diagonal quadratic form a1X12+a2X22+a3X32 where ai∈O for 1≤i≤3 for representing all 2×2 matrices over O. Let K denote a quadratic field such that its ring of integers OK is a principal ideal domain and 2 is a product of two distinct primes. It is a well-known fact that Q(−7) is the only imaginary quadratic field with the above properties. Let DK denote the discriminant of K. We have DK≡1(mod 8) if and only if 2 is a product of two distinct primes in OK. With OK as above, in this paper we generalize our earlier result. We give a necessary and sufficient condition for a diagonal quadratic form ∑i=1maiXi2 where ai∈OK, 1≤i≤m to represent all 2×2 matrices over OK. This result is a conjecture stated in [Murtuza N, Garge A. Universality of certain diagonal quadratic forms for matrices over a ring of integers, Indian Journal of Pure and Applied Mathematics, Published online; December 2022].
Automated Solution Development for Smart Grids: Tapping the Power of Large Language Models

The development of automated solutions is poten-tially crucial to enable self-healing in smart grids. Software developers will be overwhelmed with the rise of Metaverse and game industry. Therefore, it is essential to fully automate the entire software development process with the help of cutting-edge artificial intelligence (AI) technologies and advanced data analytics approaches. In this paper, we propose a new framework for automated solution development for smart grids that makes use of AI and advanced data analytics to address this challenge. Recent developments in AI technologies are used by our proposed framework to facilitate automated solution development. Our modular approach simplifies and expedites several phases of the software development process by building on the shortcomings of the current frameworks. To generate revenue, our suggested framework offers a number of business models from its adoption. We expect that our idea will inspire others to make unique contributions to the development of automated solution in smart grids to improve its operations and boost our economy.
Real-time Recognition of Indian Sign Language using OpenCV and Deep Learning

Sign language is a mechanism that uses hand gestures to facilitate communication between individuals with speaking or hearing impairments. Real-time sign language recognition provides a medium of communication between the general public and those who have difficulty with hearing or speaking. Different kinds of models are developed to provide a feasible solution for this problem. But the traditional models are either expensive or not customizable with limited gestures. In order to address this issue, a model has been developed that can recognize the sign language gestures immediately in real-time. This robust model provides an efficient way to recognize Indian Sign language (ISL) signs dynamically. The dataset is created in a customized manner to include ten phrases that convey comprehensive meaning. The captured data is augmented to identify gestures with different variations. A convolutional neural network has been employed to build the model and perform the multi-class classification on image data. The proposed model recognizes person’s gesture and provides a text output. The results and observations demonstrate that the model identifies a person’s signs accurately and efficiently in real-time. The customized model provides various advantages as new gestures can be added according to the requirement. The improvements suggest various methods that can be leveraged to upgrade the model.
A Comparison of Architectural Patterns for Testability and Performance Quality for iOS Mobile Applications Development

The first step in the design of a mobile application consists in the selection of the architecture according to the field to which the application is addressed, its characteristics and functionalities, the need for scalability or for customization in the future. Many architectural patterns are available in present but there are few empirical data that support a specific choice; therefore, this paper makes a practical thorough analysis of three of the existing architectural patterns in order to prove which of them is better to be used in iOS mobile applications development. The three architectures compared in this study are Model-View-Controller (MVC), which is Apple's recommended architecture and is also the oldest and most widely used one; Model-View-ViewModel (MVVM), which comes as a solution to the limitations of MVC; Model-View-Intent (MVI) which is used together with SwiftUI and a state machine, to achieve a reactive application considering current trends to this style of programming. Analysis of the resulting performance and testability was done by developing a case-study application in each of these three architectures. For assuring an objective comparison, performance benchmarks have been executed to monitor the memory and CPU. Aside from the performance, testability represents another key point which is analysed in the paper, by using the metrics represented by the amount of code required to implement testing the core functionalities of the application and test coverage. Finally, some conclusions that show which architectural pattern is most suitable for the iOS development were drawn.
Secure Image Retrieval of Poor Quality Images by Combining LE-GAN, Arnold Mapping and Logistic Mapping

The quantity of image data is increasing rapidly with the discovery of big data and internet technology. Currently, the majority of image retrieval techniques rely on plain text images. It is a threat to several professional fields like medicine, the military, and the government. One of the limitations of the current data model is that it is difficult to effectively retrieve images with low quality samples. LE-GAN networks can be utilized to enhance the appearance of images. Then the enhanced images are fed into the network for retrieving images securely. Using a deep artificial neural network model to extract characteristics from training data can increase the security of an image's network transmission. Then, image retrieval [1] is devised and coupled with an image encryption technique that complements and secures image retrieval [1]. The recommended method can comfy the ciphertext images' retrieval and also can increase retrieval performance. Feature extraction has accomplished the usage of AlexNet and a chaotic algorithm is used as an encryption algorithm. To safeguard the image feature facts, the encryption technique is split into components so that the image information can nevertheless be successfully covered. To enforce the feature of image encryption, Arnold Mapping, and 2D Logistic Mapping are employed.
Application of Analytical Computer Science and Technology in Data Management

The application of analytical computer science and technology in data management is a part of analytical computer science and technology. It mainly involves data management, data mining, information retrieval, text mining, etc. This course will help you understand how to use computers to solve problems related to data analysis. You will learn about the different techniques you can use when analyzing large amounts of data, such as clustering, regression analysis, classification, etc. The main purpose of this course is to let students understand various tools available in the market for analyzing large amounts of data from various sources (such as web logs or databases).