Reviewerly/exHarmony huggingface dataset:
Exploring granular test coverage and its evolution with matrix visualizations

Current software-development tools that are used in practice make understanding the test execution of software difficult, for both granular tasks (e.g., answering questions such as, “which test cases execute this method?”) and global tasks (e.g., answering questions such as, “what is the proportion of unit tests to system tests?”). Current tools typically support local, file-based views of a project’s test suite and its execution data, and rarely offer a global overview. Even more rarely, do they provide access to historical information of this nature. Such global overviews can provide a larger context for a method’s execution by test cases; help identify other similar, or related methods; and even reveal similarity between individual tests. This work approaches such challenges with a novel, interactive, matrix-based visual interface that provides a global overview of a software project’s test suite, specifically in the context of the methods available in the project’s codebase. Through a series of interactive functions to sort, filter, query, and explore a test-matrix visualization, we evaluate how developers can effectively answer questions about their project’s test suite, and the code executed by such tests. We built a dynamic test-suite analysis and software-visualization tool that implements our designed interface to address the challenges of understanding the testing of software systems. With this implementation, we conducted a user study of 20 software developers to assess their ability to understand and report test execution information and measured accuracy and time. Additionally, we present a series of case studies to demonstrate a number of insights that our tool reveals. Our evaluations, performed on 26 real-world software systems, show that the interactive visualization assisted developers to answer questions about software tests and the code they execute. Further, the visualization consistently outperforms traditional development tools, both in accuracy and time taken to complete software-engineering tasks. Global-overview test matrices offer novel perspectives on test-suite composition, which can guide software development and testing practices.
A novel finite-time complex-valued zeoring neural network for solving time-varying complex-valued Sylvester equation

A novel finite-time complex-valued zeroing neural network (FTCVZNN) for solving time-varying Sylvester equation is proposed and investigated. Asymptotic stability analysis of this network is examined with any general activation function satisfying a condition or with an odd monotonically increasing activation function. So far, finite-time model studies have been investigated for the upper bound time of convergence using a linear activation function with design formula for the derivative of the error or with variations of sign-bi-power activation functions to zeroing neural networks. A function adaptive coefficient for sign-bi-power activation function (FA-CSBP) is introduced and examined for faster convergence. An upper bound on convergence time is derived with the two components in the function adaptive coefficients of sign-bi-power activation function. Numerical simulation results demonstrate that the FTCVZNN with function adaptive coefficient for sign-bi-power activation function is faster than applying a sign-bi-power activation function to the zeroing neural network (ZNN) and the other finite-time complex-valued models for the selected example problems.
Learning to make auto-scaling decisions with heterogeneous spot and on-demand instances via reinforcement learning

Designing auto-scaling frameworks using spot and on-demand instances while considering their heterogeneity, can help Software-as-a-Service (SaaS) providers provide services with high availability to meet dynamic workloads and achieve significant cost savings. However, designing such an auto-scaling framework is difficult due to the lack of prior knowledge of the cloud. In this work, we propose an algorithm called SpotRL to solve the auto-scaling problem using heterogeneous spot and on-demand instances. Reinforcement learning (RL) approaches have been shown to be able to make effective decisions in highly dynamic environments, as they can learn step-by-step and find solutions without prior knowledge. SpotRL uses an RL-based approach for the scaling of heterogeneous spot instances. In the complex cloud environment, the training speed of RL agents is generally slow. Considering this issue, we use a multi-agent approach to decompose tasks to help agents learn faster. To reduce the negative impact of low service availability due to agents' random explorations as they interact with the cloud environment, SpotRL uses a passive approach for the scaling of heterogeneous on-demand instances. Our experimental results show that the SpotRL approach can significantly reduce the deployment cost of SaaS providers while complying with high service availability.
Virtual Model-Based Trajectory Optimization Algorithm for Aliquoting Robotic System

This work was devoted to the optimization of the trajectory of a robotic system for aliquoting biosamples, consisting of serial and parallel manipulators. The optimization consisted of two stages. At the first stage, we considered the optimization constraints associated with the workspace, taking into account the ranges of permissible values of the angles of the drive rotational joints, the link interference, and the singularities. The workspace in the space of input and output coordinates was represented as a partially ordered set of integers. At the second stage, constraints were formed related to the objects in the workspace during the aliquoting process, such as the body of the robotic system, test tubes, and racks. The condition for excluding collisions of the manipulator with other objects was provided by the geometric decomposition of objects and the exclusion of areas corresponding to external objects from the set describing the workspace of the manipulator. Optimization was performed in the space of input coordinates. The objective function was proportional to the duration of movement along the trajectory. The possibility of applying evolutionary algorithms to solve this problem is analyzed. An assessment of the performance is given. The optimization and export of the resulting trajectory were implemented in software, which enabled verification of the optimization results on a virtual model. The simulation results are presented.
Addressing Extreme Market Responses Using Secure Aggregation

An investor short sells when he/she borrows a security and sells it on the open market, planning to buy it back later at a lower price. That said, short-sellers profit from a drop in the price of the security. If the shares of the security instead increase in price, short sellers can bare large losses. Short interest stock market data, provide crucial information of short selling in the market for data mining by publishing the number of shares that have been sold short. Short interest reports are compiled and published by the regulators at a high cost. In particular, brokers and market participants must report their positions on a daily basis to Financial Industry Regulatory Authority (FINRA). Then, FINRA processes the data and provides aggregated feeds to potential clients at a high cost. Third party data providers offer the same service at a lower cost given that the brokers contribute their data to the aggregated data feeds. However, the aggregated feeds do not cover 100% of the market since the brokers are not willing to submit and trust their individual data with the data providers. Not to mention that brokers and market participants do not wish to reveal such information on a daily basis to a third party.
Computing for climate resilience in agriculture

research-article Free AccessComputing for climate resilience in agriculture Authors: Hemant Belsare IIT Bombay, Mumbai, India IIT Bombay, Mumbai, IndiaView Profile , Milind Sohoni IIT Bombay, Mumbai, India IIT Bombay, Mumbai, IndiaView Profile , Rahul Gokhale IT consultant, Mumbai, India IT consultant, Mumbai, IndiaView Profile , Pooja Prasad IHE Delft, The Netherlands IHE Delft, The NetherlandsView Profile , Asim R. P. IIT Bombay, Pune, India IIT Bombay, Pune, IndiaView Profile , Chirag Marathe IIT Bombay, Mumbai, India IIT Bombay, Mumbai, IndiaView Profile , Parth Gupta GIZ India, New Delhi GIZ India, New DelhiView Profile , Shubhada Sali IIT Bombay, Mumbai, India IIT Bombay, Mumbai, IndiaView Profile , Swapnil Patil Oracle in Bangalore, India Oracle in Bangalore, IndiaView Profile , Sudhanshu Deshmukh Tagbox Solution Pvt. Ltd. in Bangalore, India Tagbox Solution Pvt. Ltd. in Bangalore, IndiaView Profile Authors Info & Claims Communications of the ACMVolume 65Issue 11November 2022 pp 74–79https://doi.org/10.1145/3554928Published:20 October 2022Publication History 0citation5,499DownloadsMetricsTotal Citations0Total Downloads5,499Last 12 Months5,499Last 6 weeks1,437 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
AI and data science centers in top Indian academic institutions

research-article Free AccessAI and data science centers in top Indian academic institutions Authors: B. Ravindran Indian Institute of Technology Madras, India Indian Institute of Technology Madras, IndiaView Profile , Sunita Sarawagi Indian Institute of Technology Bombay, India Indian Institute of Technology Bombay, IndiaView Profile , Aditi Jain Indian Institute of Technology Madras, India Indian Institute of Technology Madras, IndiaView Profile Authors Info & Claims Communications of the ACMVolume 65Issue 11November 2022 pp 94–97https://doi.org/10.1145/3556634Published:20 October 2022Publication History 0citation4,535DownloadsMetricsTotal Citations0Total Downloads4,535Last 12 Months4,535Last 6 weeks1,439 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
DancingPottery: Posture-Driven Pottery Generative Design and Fabrication

We present a method of designing and fabricating pottery artwork through human posture where the users need neither professional skills nor experience with 3D modeling software. This method creatively solves the problem of mapping between the human skeleton and the pottery shape and has a strong ability to model complex shapes while being user-friendly. Our system represents the deformation of a pottery object with four independent operators and provides real-time visual feedback as the user changes their posture. Unlike traditional pottery throwing, where the product has high symmetry, our system supports modeling asymmetric pottery shapes. After obtaining the model, the model can be fabricated directly via ceramic 3D printing, as the models satisfy printability constraints. A user study showed that the designed mapping relationship supports pottery shape deformation with a high degree-of-freedom, and inexperienced users can easily use the system with minimal instruction.
Decentralization Analysis of Pooling Behavior in Cardano Proof of Stake

Blockchain protocols’ main differentiator is their purported decentralization that unlocks various information technology applications that were supposedly impossible beforehand. The key promise is that incentive-driven participation of a large set of interested parties can lead to decentralized protocol states where no single operator can be a “single point of failure.” Despite this promise, there is little systematic analysis of decentralization in blockchain systems and the sporadic theoretic and empirical investigations that exist paint a rather negative picture due to resource “pooling behaviors” that are impossible to prevent in the “permissionless” setting of such protocols where parties have no designated identities.
Deep Learning-based Electric Kickboard Helmet Detection under Night Driving Conditions

As the use of electric kickboards has increased recently, the frequency of safety accidents is also increasing. Even though the government amended the road traffic law to make it compulsory to wear a helmet, the helmet-wearing rate is still remarkably low. In this work, we propose a novel method to monitor and detect whether a user of an electric kickboard is wearing a helmet through a deep learning-based object detection network, thereby increasing the helmet-wearing rate. For continuous driver monitoring, the proposed method captures the user’s face in a front view using the user’s mobile phone attached to the kickboard. In addition, to handle both day and night driving conditions, various image conversion, enhancement, and augmentation techniques were considered and evaluated. The experimental results showed the feasibility of the proposed method.
Super-Resolving Methodology for Noisy Unpaired Datasets

Although it is possible to acquire high-resolution and low-resolution paired datasets, their use in directly supervised learning is impractical in real-world applications. In the present work, we focus on a practical methodology for image acquisition in real-world conditions. The main method of noise reduction involves averaging multiple noisy input images into a single image with reduced noise; we also consider unpaired datasets that contain misalignments between the high-resolution and low-resolution images. The results show that when more images are used for average denoising, better performance is achieved in the super-resolution task. Quantitatively, for a fixed noise level with a variance of 60, the proposed method of using 16 images for average denoising shows better performance than using 4 images for average denoising; it shows 0.68 and 0.0279 higher performance for the peak signal-to-noise ratio and structural similarity index map metrics, as well as 0.0071 and 1.5553 better performance for the learned perceptual image patch similarity and natural image quality evaluator metrics, respectively.
Effect of Climate on Photovoltaic Yield Prediction Using Machine Learning Models

Machine learning is arising as a major solution for the photovoltaic (PV) power prediction. Despite the abundant literature, the effect of climate on yield predictions using machine learning is unknown. This work aims to find climatic trends by predicting the power of 48 PV systems around the world, equally divided into four climates. An extensive data gathering process is performed and open-data sources are prioritized. A website www.tudelft.nl/open-source-pv-power-databases has been created with all found open data sources for future research. Five machine learning algorithms and a baseline one have been trained for each PV system. Results show that the performance ranking of the algorithms is independent of climate. Systems in dry climates depict on average the lowest Normalized Root Mean Squared Error (NRMSE) of 47.6 %, while those in tropical present the highest of 60.2 %. In mild and continental climates the NRMSE is 51.6 % and 54.5 %, respectively. When using a model trained in one climate to predict the power of a system located in another climate, on average systems located in cold climates show a lower generalization error, with an additional NRMSE as low as 5.6 % depending on the climate of the test set. Robustness evaluations were also conducted that increase the validity of the results.
Peculiarities of Electron Wave Packet Dynamics in Planar Nanostructures in the Presence of Magnetic and Electric Fields

Currently, spatially localized electron densities and currents are considered to be candidates for use in the encoding of quantum information. For this reason, the control of their temporal dynamics is an important task. In this work, the spatiotemporal evolution of an electron wave packet in planar nanostructure in the presence of transverse magnetic and lateral electric fields is investigated by direct analytical solution of the non-stationary Schrödinger equation. Methods to control and manage the dynamics of the spatially localized electron density distribution are developed. The production of photon-like quantum states of electrons opens up opportunities for applications similar to quantum optical and quantum information technologies but implemented with charge carriers. Quantum control of the trajectory of the electron wave packet, accompanied by dramatic suppression of its spreading, is demonstrated. This study discovered methods to manage spatially localized electron behavior in a nanostructure that allows a controllable charge quantum transfer and gives rise to new prospects for quantum nanoelectronics technology.
The Disciplinary Identity of HCI Research: An Investigation Using Configurational Theory

HCI is an eclectic intellectual community comprised of researchers from different academic backgrounds applying diverse methodologies to an array of research questions. What is it that unites this community? What makes HCI an academic discipline? Researchers have proposed different frames—science and problem-solving, for example—to make sense of HCI as a unified, cohesive discipline. In this paper, we investigate whether a configurational theory of HCI can contribute to the discourse. As part of our investigation, we propose a configurational theory that is potentially suitable for HCI research. Our examination has led to an understanding of HCI that emphasizes its core object of study rather than its methodologies or tendency towards problem solving. We explore how this approach complements and extends existing discussions of HCI’s disciplinary identity. We end by discussing some potential benefits that a configurational theory might have, such as its ability to serve as a frame of reference for researchers, help researchers position their work in relation to existing work, identify and evaluate research claims and evidence, and assess contribution and knowledge growth in the field.
A model to reduce insider cybersecurity threats in a South African telecommunications company

Background: Cybersecurity breaches have become a growing challenge in today’s digital economy. Organisations are faced with the responsibility of protecting their information resources from cybersecurity threats, and insider threats are one of them. Organisations have sophisticated technologies to protect themselves against these attacks, and their employees are often less guarded when it comes to protecting valuable company information systems. Objectives: This research was aimed to develop and conceptualise a model to reduce cybersecurity insider threats in a South African telecommunication organisation. Method: This study was conducted using a survey research approach, where close-ended questionnaires were utilised to collect data from respondents. The collected data was then analysed using IBM Statistical Package for Social Science (SPSS). Results: The findings of the study indicated that personal norms in the domain of cybersecurity have a positive influence on individuals’ attitude towards engaging in cybersecurity misbehaviour, and this has a significant relationship with their reduction of insider threats (RIT). Conclusion: This study suggests that management should give close and thoughtful attention to factors that encourage their employees to engage in cybersecurity misbehaviour. As an efficient and effective approach to mitigate the risk of cybersecurity insider threats, identification and classification of these factors should be followed by proper planning with a goal of reducing their negative effect on employees’ behaviour.
Open Rules for Cataloging

The Open Rules for Cataloging (ORC) project is proposed to be a freely available alternative to Resource Description and Access (RDA) for those who desire a simpler, less abstract cataloging code. The premise of the ORC project is that it is possible to identify universal cataloging concepts in preexisting cataloging rule sets which can be used as the base of a modern cataloging code. The ORC Project has three goals in mind: to reduce needless expense, to promote inclusion, and to facilitate data interoperability. ORC complies with ethical principles and best practices. It will be compatible with a variety of metadata environments. This compatibility will enable data exchange and reuse as well as adaptation. The Open Rules for Cataloging have the potential to revolutionize cataloging practice by simplifying it and making it accessible to all.
Simple principles behind the mathematics and some essential concepts

Null hypothesis tests take the difference between our prediction and our outcome, sample size, and sometimes the amount of variation in our data, and combine them into a single number that can be used to find p. Numerical data are measurements or counts that can be averaged and which display scatter, variance, around that average. Analysis of variance and t tests are commonly used to analyze numerical data because those tests incorporate variance into their outcomes. Nominal data are counts of observations within categories. With nominal data, there is no variance and no way to calculate an average. Nominal data are typically analyzed with chi-squared or other similar tests. Regardless of the type of data, the product of a null hypothesis test is a single number that summarizes key aspects of our data in a way that lets us gauge the trustworthiness of those data. The mathematics are introduced.
A comparison on type-II fuzzy logic based data encryption techniques in the Internet of Things

Smart gadgets have created a buzz in the market today; you will find everything smart today. Like a smartwatch, smart band, smart led, smart heater, etc., and transmitting data securely between all these devices is necessary as an outcome; IoT devices developed defenseless to numerous devices. Faith replicas were predictable, significant simultaneous approaches to defend a large communication system in contrast to evil virtual outbreaks. In this research paper, the various Type-II fuzzy logic models are evaluated, which provides enhanced data security for IoT devices. Also, compression is applied between all data encryption techniques based on the parameters like Reproduction time (circles), Program series (m), Quantity of device nodes, Number of spiteful nodes, and Total interval.
Self-Adaptation Through Reinforcement Learning Using a Feature Model

Typically, self-adaptation is achieved by implementing the MAPE-K Control Loop. The researchers agree that multiple control loops should be assigned if the system is complex and large-scale. The hierarchical control has proven to be a good compromise to achieve SAS goals, as there is always some degree of decentralization but it also retains a degree of centralization. The decentralized entities must be coordinated to ensure the consistency of adaptation processes. The high cost of data transfer between coordinating entities may be an obstacle to achieving system scalability. To resolve this problem, coordination should only define between entities that require communication between them. However, most of the current SAS relies on static MAPE-K. In this article, authors present a new method that allows changing the structure and behavior of loops. Authors base on exploration strategies for online reinforcement learning, using the feature model to define the adaptation space.