Reviewerly/exHarmony huggingface dataset:
From Service-Orientation to Agile Development by Conceptually Linking Business IT Services and User Stories: A Meta-Model and a Process Fragment

IT services are being built by IT departments and other service providers to address the core technological requirements of organizations. Even if these services solve operational issues, their adoption – because of their internal behavior – has a sustaining or non-sustaining impact on the (long term) strategy of the organization. This impact is called the Business and IT alignment (BITA); as such, it is problematic to estimate before the development of the service without a view on its design. To be properly done, BITA evaluation indeed needs details about the service run-time behavior. Conversely, the agile wave refrains from a detailed upfront software design; functions providing high operational value to users are indeed selected and built for one sprint at a time. The mismatch between a traditional service approach and an agile way of development is therefore quite obvious. The present paper proposes a method (called Agile-MoDrIGo) to reconcile these two approaches. For this purpose, it (i) decomposes services into epic user stories under the scope of which (user-level built) user stories can be mapped (i.e. functional alignment) and (ii) links services as (coarse-grained) conceptual elements along with the strategic objectives they sustain to fine-grained user story functional elements for value-driven prioritization (i.e. strategic alignment). The proposal is presented in the form of a meta-model and supported by a process fragment; it is validated on a case study in healthcare.
Research of Electric Contact Welding by a Wire at Restoration of Details of Cars

The article presents the results of a study of electrical contact welding with a wire when restoring the working surface of machine parts.In order to test the effectiveness of the developed measures to prevent interlayer fractures, the samples after multilayer welding and rough grinding were subjected to embossing with an impact energy of 6 J. The thickness of the layer after rough grinding was 1.5 mm. The depth of hardening, determined by measurements of microhardness, was 3.5 … 4 mm. Metallographic studies of interlayer fractures of the multilayer coating did not reveal. It was found that the cause of continuity defects in the "scallops" of the welded metal layer is the increased current density in the cross section of the upset wire from the side of the previously welded roller. This leads to local overheating of a part of the filler metal, its squeezing out from under the electrode and cooling on the surface of the previously welded bead in the absence of forging. An increase in the width of the working part of the roller electrode over a certain value, depending on the diameter of the filler wire, the amount of its upsetting, the welding step, prevents the upsetting wire from being squeezed out from under the electrode and eliminates the reasons for the formation of these defects. An increase in the torque of the roller electrode leads to an increase in the strength of the formed welded joint due to an increase in the relative axial deformation of the filler wire. It was found that an increase in the relative axial deformation of the filler wire leads to a slowdown in the rotation of the roller electrode. This makes it possible to control the relative axial deformation of the filler wire directly during the welding process by counting the number of revolutions of the roller electrode and the part.
An Evolutionary Analysis of Software-Architecture Smells

If software quality assurance is postponed or even abandoned for a software system, maintenance and evolution become harder or even impossible. One widely known symptom for the degradation of system quality are Architecture Smells (ASs), which violate fundamental principles of software design. In this paper, we present a study on the evolution of ASs as well as on how and when they foster system degradation. Thus, we provide valuable insights regarding what ASs are meaningful to assure system quality. To this end, we analyzed the evolution of three types of ASs in 14 open-source systems with a total of 485 versions. We adapted indicators used in previous studies to assess the severity of ASs (e.g., growth, lifetime), and relate ASs to technical debt as another established indicator. Our results indicate that 1) ASs remain mostly stable compared to the code size of a system, 2) certain types of ASs, such as cyclic dependencies, have a greater impact on system degradation, and 3) certain properties determine how much an AS contributes to software degradation. These findings are valuable for practitioners to identify and tackle system degeneration, as well as for researchers to scope new research on managing ASs and technical debt.
Single Person Dense Pose Estimation via Geometric Equivariance Consistency

We study the task of single person dense pose estimation. Specifically, given a human-centric image, we learn to map all human pixels onto a 3D, surface-based human body model. Existing methods approach this problem by fitting deep convolutional networks on sparse annotated points where the regression on both surface coordinate components for each body part is uncorrelated and optimized separately. In this work, we devise a novel, unified loss function that explicitly characterizes the correlation for surface coordinates regression, achieving significant improvements in both accuracy and efficiency. Furthermore, based on an observation that the image-to-surface correspondence is intrinsically invariant to geometric transformations from input images, we propose to enforce a geometric equivariance consistency on the target mapping, thereby allowing us to enable reliable supervision on large amounts of unlabeled pixels. We conduct comprehensive studies on the effectiveness of our approach using a quite simple network. Extensive experiments on the DensePose-COCO dataset show that our model achieves superior performance against previous state-of-the-art methods with much less computation complexity. We hope that our work would serve as a solid baseline for future study in the field. The code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Johnqczhang/densepose.pytorch</uri> .
Isolated Gujarati Handwritten Character Recognition (HCR) using Deep Learning (LSTM)

With the swiftly escalating the paperless and automated offices and governance we required to convert paper into digital form. HCR is the form of optical character recognition to recognition the printed or handwritten text into digital text. The writing style of a person, size and thickness of the characters are different from person to person hence HCR is more challenging for automated system. As concern with Gujarati HCR there is more requirement to develop such an automated system that can be used for Gujarati Character Recognition with accents. [1]. In this study author have focused to develop LSTM model for offline Gujarati Character Recognition. Along with this an attempt to improve the rate of Gujarati character recognition using the LSTM model with the help of teaching and learning process of model with the given dataset of almost 58,000 images. The novelty of this proposed system is to identify a complete set of Guajarati characters that are available with the Unicode dataset. Author have used LSTM model in this study and achieved ~ 97% success rate of each character class.
Sensitivity Analysis of the Composite Data-Driven Pipelines in the Automated Machine Learning

In the paper, the flexible approach for sensitivity analysis of composite data-driven modeling pipelines is proposed. It allows analyzing the structural properties of the graph-based models with complex heterogeneous structures. This approach can be used in different ways: as a part of AutoML solutions and as a custom tool for pipeline design. The results of the experiments confirm the effectiveness of the proposed approach for the improvement of the pipeline’s effectiveness in various benchmarks for regression and classification tasks. The software implementation is available as a part of the open-source framework FEDOT.
Self-Awareness as a Prerequisite for Self-Adaptivity in Computing Systems

Establishing a better understanding of self-aware and self-adaptive systems is the first step towards specifying, modelling, designing and engineering these systems in the future. Although there might be some intuition behind the notions of awareness and adaptivity, there is a lack of clear definition and differentiation of these terms. In particular, the notion of awareness has been extensively studied in psychology and philosophy; however, a more rigorous understanding of this terminology is necessary for scientific debates in engineering. In this paper, by giving insights into how self-adaptive systems differ from the ordinary systems that are considered as non-adaptive, we set the foundation for understanding and differentiating self-awareness and self-adaptivity as two correlated but still different terms. We identify the system's self-awareness as a prerequisite for self-adaptivity and define two levels of awareness in computing systems.
Quantum gates robust to secular amplitude drifts

The robustness of quantum gates in the presence of imperfections in the classical control fields that drive the gates is an important issue in quantum computation. The authors extend the technique of composite pulses and construct two pulse sequences that provide robustness against time-dependent drift in the amplitude of the control pulses.
Distributing Graph States Across Quantum Networks

Graph states form an important class of multipartite entangled quantum states. We propose a new approach for distributing graph states across a quantum network. We consider a quantum network consisting of nodes—quantum computers within which local operations are free—and EPR pairs shared between nodes that can continually be generated. We prove upper bounds for our approach on the number of EPR pairs consumed, completion time, and amount of classical communication required, all of which are equal to or better than that of prior work [10]. We also reduce the problem of minimizing the completion time to distribute a graph state using our approach to a network flow problem having polynomial time complexity.
Children’s query formulation and search result exploration

Our research aims at understanding children's information search and their use of information search tools during educational pursuits. We conducted an observation study with 50 New Zealand school children between the ages of 9 and 13 years old. In particular, we studied the way that children constructed search queries and interacted with the Google search engine when undertaking a range of educationally appropriate inquiry tasks. As a result of this in situ study, we identified typical query-creation and query-reformulation strategies that children use. The children worked through 250 tasks, and created a total of 550 search queries. 64.4% of the successful queries made were natural language queries compared to only 35.6% keyword queries. Only three children used the related searches feature of the search engine, while 46 children used query suggestions. We gained insights into the information search strategies children use during their educational pursuits. We observed a range of issues that children encountered when interacting with a search engine to create searches as well as to triage and explore information in the search engine results page lists. We found that search tasks posed as questions were more likely to result in query constructions based on natural language questions, while tasks posed as instructions were more likely to result in query constructions using natural language sentences or keywords. Our findings have implications for both educators and search engine designers.
Blockchain and Multiple Linear Regression-based Energy Trading Scheme for Electric Vehicles

This paper proposes a blockchain-based secure energy trading platform for electric vehicles (EVs) in a smart grid platform. EVs can be either prosumer (generates and consumes energy) or consumer. The proposed system mainly focuses on securing the energy demand data of EVs. It employs sixth-generation (6G) as a communication network that ensures ultra-low latency, high reliability, and scalable communication. We employed a multiple linear regression (MLR) model to analyze the energy demand data and predict EV energy consumption. Smart grid optimizes the energy for EVs that maximizes the profit for both prosumer and consumer EVs. The performance analysis of the proposed system with the 6G is evaluated by comparing it with the traditional 5G and 4G networks. Various performance metrics that have been considered for assessing the proposed scheme are latency, scalability, and profit for EVs. The performance results and analysis show that the proposed system is reliable, secure, and cost-efficient for both EVs compared to traditional energy trading systems.
Review of autonomous inspection technology for power lines using UAVs

With the continuous expansion of the scale of the power grid and the gradual maturity of the application of drones, drones with the advantages of flexibility and convenience can overcome terrain constraints while reducing manpower and safety risks and can be well qualified for the power inspection work under the large-scale development of the power grid and improve the inspection efficiency. However, the massive amount of data collected by UAV power line inspection relies on the human judgment through the naked eyes of staff, and the recognition efficiency is very low and the accuracy rate is subjectively influenced. In recent years, the gradual introduction of traditional machine learning and deep learning has led to significant improvements in inspection efficiency and accuracy. The autonomous obstacle avoidance technology based on the inherent electromagnetic field characteristics of power lines makes it possible for UAVs to cruise autonomously. Based on a systematic summary of the achieved results, this paper briefly reviews the overall development of both power component detection and autonomous cruise in UAV power line inspection. At the same time, the current problems and future research trends are described, expecting to provide references for further research in this field.
Secure Storage of Electronic Medical Records(EMR) on Interplanetary File System(IPFS) Using Cloud Storage and Blockchain Ecosystem

Electronic Medical Records(EMR) are an important aspect of any healthcare system. The effective and secure management of the same is an integral task. With new and upcoming technologies, there have been various new mechanisms to securely store patient information. It is a time critical task to retrieve and refer to a patient’s medical record for effective and rapid treatment. Due to the large number of Electronic Medical Records(EMR) owing to the large healthcare sector, there exists high risk of possible intrusions. It is also one of the most targeted sectors for personal data theft. This paper proposes a secure system for storage and access of these Electronic Medical Records(EMR) taking into consideration the confidentiality, integrity and availability of these records. The paper proposes a system for access of a centralised storage system for healthcare institutions around the globe, taking into account the secure features of a decentralised Interplanetary File System(IPFS). The storage system utilises the Blockchain Ecosystem for storing sensitive data. This system also implements a Role Based Access Control(RBAC) system for access to the storage. The paper shows implementation of the system through thorough breakdown of the system.
A Study on Natural Language Chatbot-based Authentication Systems

The field of Natural Language Understanding has many powerful applications across a variety of domains. We seek to create a novel authentication system that utilizes Natural Language Understanding technologies to create a chatbot that asks dynamically-generated questions and compares the user&#x2019;s response to the expected answer. We detail the necessary requirements for this system and examine recent developments in the fields of Natural Language Understanding and chatbot technologies to determine whether it can be implemented by any extant works. We then describe how this system can be expected to operate and what purpose it serves as an authentication technology.
I Know What You Need: Investigating Document Retrieval Effectiveness with Partial Session Contexts

Reducing user effort in finding relevant information is one of the key objectives of search systems. Existing approaches have been shown to effectively exploit the context from the current search session of users for automatically suggesting queries to reduce their search efforts. However, these approaches do not accomplish the end goal of a search system—that of retrieving a set of potentially relevant documents for the evolving information need during a search session. This article takes the problem of query prediction one step further by investigating the problem of contextual recommendation within a search session. More specifically, given the partial context information of a session in the form of a small number of queries, we investigate how a search system can effectively predict the documents that a user would have been presented with had he continued the search session by submitting subsequent queries. To address the problem, we propose a model of contextual recommendation that seeks to capture the underlying semantics of information need transitions of a current user’s search context. This model leverages information from a number of past interactions of other users with similar interactions from an existing search log. To identify similar interactions, as a novel contribution, we propose an embedding approach that jointly learns representations of both individual query terms and also those of queries (in their entirety) from a search log data by leveraging session-level containment relationships. Our experiments conducted on a large query log, namely the AOL, demonstrate that using a joint embedding of queries and their terms within our proposed framework of document retrieval outperforms a number of text-only and sequence modeling based baselines.
Adopting microservice architecture: A decision support model based on genetically evolved multi-layer FCM

Microservice architectures foster the development of applications as suites of small, autonomous and conversational services, which are then easy to understand, deploy and scale. However, one of the problems nowadays is that microservices introduce new complexities to the system and, despite the hype, many factors should be considered when deciding whether to use them or not. This paper introduces a novel decision and analysis model with enhanced interpretative and explanatory capabilities. The model is conceived by identifying the key concepts and factors in deciding whether to adopt microservice architectures, or not, through literature review and experts’ feedback from the industry and academia. These concepts are organized as a Multi-Layer Fuzzy Cognitive Map (MLFCM), a graph-based computational intelligent model. A new formulation is proposed, along with a novel genetically evolved algorithm, both aiming at improving the model in terms of performance, bias resilience and explainability. The model is evaluated and calibrated through a series of executions over real and synthetic scenarios. The application of static and dynamic analyses, in conjunction with the incorporation of the evolutionary approach, guide the identification of the prevailing factors that regulate the adoption of a microservice architecture and allow the interpretation of the importance of each concept. Finally, an industrial scenario leverages the assessment of the model’s applicability and efficacy, highlighting some interesting results.
A novel fuzzy and reverse auction‐based algorithm for task allocation with optimal path cost in multi‐robot systems

Abstract One of the most frequent issues in multiple robot implementation is task allocation with the lowest path cost. Our study addresses the multi‐robot task allocation challenge with path costs, the lowest computing time, and task distribution. Furthermore, it is usual for a robot's processing capabilities to be restricted to operate in various target environments. As a consequence, adequate processing power consumption would demonstrate the system's efficiency. Task allocation and path planning issues must be addressed regularly to ensure multi‐robot system operation. Task allocation and path planning issues must be addressed regularly to ensure multi‐robot system operation. The above‐mentioned serious challenge gets more complicated when system factors such as robots and tasks multiply. As previously stated, this article solves the issue using a fuzzy‐based optimum path and reverse auction‐based methods. The detailed simulation results indicate that the suggested methods can solve task allocation with the lowest path cost. A comparative study is conducted between the suggested algorithm and two existing commonly used techniques, the auction‐based and the Hungarian algorithms. Finally, the suggested method was run in real‐time on a TurtleBot2 robot. The findings show the suggested algorithm's efficiency and simplicity of implementation.
Knowledge triggering, extraction and storage via human–robot verbal interaction

This article describes a novel approach to expand in run-time the knowledge base of an Artificial Conversational Agent. A technique for automatic knowledge extraction from the user's sentence and four methods to insert the new acquired concepts in the knowledge base have been developed and integrated into a system that has already been tested for knowledge-based conversation between a social humanoid robot and residents of care homes. The run-time addition of new knowledge allows overcoming some limitations that affect most robots and chatbots: the incapability of engaging the user for a long time due to the restricted number of conversation topics. The insertion in the knowledge base of new concepts recognized in the user's sentence is expected to result in a wider range of topics that can be covered during an interaction, making the conversation less repetitive. Two experiments are presented to assess the performance of the knowledge extraction technique, and the efficiency of the developed insertion methods when adding several concepts in the Ontology.
DeepScanner: a Robotic System for Automated 2D Object Dataset Collection with Annotations

In the proposed study, we describe the possibility of automated dataset collection using an articulated robot. The proposed technology reduces the number of pixel errors on a polygonal dataset and the time spent on manual labeling of 2D objects. The paper describes a novel automatic dataset collection and annotation system, and compares the results of automated and manual dataset labeling. Our approach increases the speed of data labeling 240-fold, and improves the accuracy compared to manual labeling 13-fold. We also present a comparison of metrics for training a neural network on a manually annotated and an automatically collected dataset.