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We report the development and testing of new integrated cyberinfrastructure for performing free energy simulations with generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and machine learning potentials (MLPs) in Amber. The Sander molecular dynamics program has been extended to leverage fast, density-functional tight-binding models implemented in the DFTB+ and xTB packages, and an interface to the DeePMD-kit software enables the use of MLPs. The software is integrated through application program interfaces that circumvent the need to perform "system calls" and enable the incorporation of long-range Ewald electrostatics into the external software's self-consistent field procedure. The infrastructure provides access to QM/MM models that may serve as the foundation for QM/MM–ΔMLP potentials, which supplement the semiempirical QM/MM model with a MLP correction trained to reproduce ab initio QM/MM energies and forces. Efficient optimization of minimum free energy pathways is enabled through a new surface-accelerated finite-temperature string method implemented in the FE-ToolKit package. Furthermore, we interfaced Sander with the i-PI software by implementing the socket communication protocol used in the i-PI client–server model. The new interface with i-PI allows for the treatment of nuclear quantum effects with semiempirical QM/MM–ΔMLP models. The modular interoperable software is demonstrated on proton transfer reactions in guanine-thymine mispairs in a B-form deoxyribonucleic acid helix. The current work represents a considerable advance in the development of modular software for performing free energy simulations of chemical reactions that are important in a wide range of applications. [ABSTRACT FROM AUTHOR]

Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. We present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet. [ABSTRACT FROM AUTHOR]

Recent developments indicate that malware programs present a significant risk in the security and privacy of cloud systems. Existing research in malware detection encounters numerous significant challenges due to the constantly changing and advanced characteristics of malware. Malware detection systems frequently experience high rates of false positives and false negatives, where legitimate applications are incorrectly identified as malware or actual malware remains undetected, which results in operational inefficiencies. Traditional signature-based approaches struggle in recognizing new or modified malware. Additionally, sophisticated malware types, such as file less malware, ransom ware, and rootkits pose detection challenges as they integrate deeply into systems or alter their behaviour to evade detection. These challenges highlight the urgent need for ongoing advancements in this field. Existing methods of malware detection that rely on signatures have been found to be both inefficient and slow in the context of cloud environments. Also, Existing studies have focused on detecting malware by analysing input API calls. But, these models have encountered challenges such as limited accuracy and difficulties in effectively classifying malware types. In contrast, Deep Learning (DL) have shown success by analysing malware behaviour through API calls, which yields encouraging results. Additionally, the data produced by API calls necessitates more computational resources for training. To address these challenges, a new deep learning-based malware detection approach utilizes 2D grayscale images derived from API calls, along with an effective tuning strategy has been proposed. Initially, data are collected from cloud malware dataset. Then, API calls are converted into 2D gray scale images in order to construct gray scale image dataset. After getting the gray scale image, pre-processing is performed to reduce high level noise and to enhance the quality of image by weighted mean filter and anisotropic filter, which helps to improve the performance in classification. Next, these images are then passed into the feature extraction stage to extract sufficient features with an effective integrated densely connected squeeze MobileNet v2 (Ef-DeSMob2), which reduces the dimensionality issue and increase the computational complexity. Then, the collected features are passed into the classification phase to detect normal and malware classes from the samples using sparse attention with residual pyramidal depth wise separable convolutional neural networks (SA:ResPyDSC), which focus to enhance the security and reliability of the model. Finally, the hyper parameters in the classifier model like weights, bias are properly fine-tuned by utilizing hybrid white shark beluga optimization algorithm (Hy-WBeOp). The experimental findings illustrate that the proposed method attains accuracy of 98.06%, precision of 97.99%, recall of 97.05%, f1-score of 96.08% and error metrics like MSE AT 0.08, RMSE of 0.27 and MAE of 0.21, which shows that the proposed method helps to classify the malware classes accurately with less error rates. The proposed approach outperforms with the existing techniques because of its great efficiency. Overall, this approach establish a strong malware detection system classification and enhance the reliability and effectiveness of protection against malicious attacks. [ABSTRACT FROM AUTHOR]

Microscopy has been pivotal in improving the understanding of structure‐function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human‐centric click‐and‐go paradigm utilizing vendor‐provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, a coupled software–hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field‐programmable‐gate‐array device with LabView‐built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user‐defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine‐learning libraries and simulations, to provide automated decision‐making and active theory‐experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery. [ABSTRACT FROM AUTHOR]

Migrating from Innovative's Sierra to OCLC WorldShare during the COVID-19 quarantine was the incentive to try a novel approach to transferring open serial and monograph orders using WorldShare's application programming interfaces (APIs). At a time when staff training and supervision would have been difficult or impossible, and in the absence of a standard procedure for migrating acquisitions data to WorldShare, we were nevertheless able to accomplish this task with a small team working remotely. Instead of recreating orders manually in the new system, we took advantage of the APIs and the flexible structure of order records in WorldShare to systematically and reliably migrate data exported from Sierra. [ABSTRACT FROM AUTHOR]

In 2021, a team of developers from the Deutscher Wetterdienst (DWD), the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR), the German Climate Computing Center (Deutsches Klimarechenzentrum, DKRZ) and the Forschungszentrum Jülich (FZJ) started the Icosahedral Nonhydrostatic (ICON) Model Community Interface (ComIn) project: ICON ComIn is a library with multi-language support for connecting third-party modules ('plugins') to the ICON model using the dynamic loader of the operating system. ComIn is intended for a wide range of use cases, from the integration of simple diagnostic Python scripts to full chemistry models into ICON. ICON ComIn is distributed with the ICON model code under an open source license. Its application programming interface (API) provides a low barrier for code extensions to ICON and reduces the migration effort in response to new ICON releases. ComIn's main design principles are that it is lightweight, interoperable (Fortran, C/C++, Python) and flexible, and required changes in ICON are minimised. During the development of ComIn the ease of getting started and the experience during plugin development were guiding principles to provide a convenient tool. The extensive documentation and a variety of test and example plugins are results of this process. This paper motivates the underlying design principles and provides some concrete reasoning for their selection. Further, current limitations are discussed and the vision for the future is presented. [ABSTRACT FROM AUTHOR]

PurposeMethodologyFindingsImplicationsLivestreaming commerce is becoming increasingly frequent, and this rapid development has benefited from ever-increasing numbers of influencers joining businesses. Given this context, the purpose of this study is to examine the nonlinear effect of influencers’ beauty on viewer purchases.Based on the unstructured data of 2,597 livestreaming clips from TikTok, we first leverage a computer vision-based API interface, Face++, to calculate influencer beauty. Economic models are used to investigate this relationship. Second, the Sobel test is used to test the mediating effects of LSTM-based influencer trustworthiness. Finally, two moderators, product category and influencer type, are verified to clarify the boundary conditions.First, there is an inverted U-shaped relation between influencer beauty and sales rate. Second, influencer trustworthiness mediates the impact of influencer beauty on sales rate. Finally, we find that compared with newcomer influencer selling experience products, veteran influencer beauty has a greater impact on sales when selling search products.First, to the best of our knowledge, this is one of the few empirical studies evaluating influencer beauty through video data. More broadly, our research contributes to the rich influencer marketing literature by taking advantage of the abundant visual information provided by the new business context of livestreaming. The second is the new method of computer vision and multimodal machine learning. The combination of these new methods provides a scalable toolkit for unstructured data mining for academia and a realistic reference for livestreaming commerce for industry. The third contribution is content validity. We depend on hard archival data, which reflect changes in influencer beauty and viewers’ purchases in real time. This study extends the external validity of past studies and has greater reference value for real livestreaming businesses. [ABSTRACT FROM AUTHOR]

In this manuscript I build upon an initial body of research developing procedures for leveraging large language models (LLMs) in qualitative data analysis, by carrying out thematic analysis (TA) with LLMs. TA is used to identify patterns by means of initial labelling of qualitative data followed by the organisation of the labels/codes by themes. First, I propose a new set of LLM prompts for initial coding and generation of themes. These new prompts are different from the typical prompts deployed for such analysis in that they are entirely open-ended and rely on TA language. Second, I investigate the process of removing duplicate initial codes through a comparative analysis of the codes of each interview against a cumulative codebook. Third, I explore the construction of thematic maps from the themes elicited by the LLM. Fourth, I evaluate the themes produced by the LLM against the themes produced manually by humans. For conducting this research, I employed a commercial LLM via an application program interface (API). Two datasets of open access semi-structured interviews were analysed to demonstrate the methodological possibilities of this approach. I conclude with practical reflections on performing TA with LLM, enhancing our knowledge of the field. [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation. [ABSTRACT FROM AUTHOR]

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments. [ABSTRACT FROM AUTHOR]

System-to-system communication via Application Programming Interfaces (APIs) plays a pivotal role in the seamless interaction among software applications and systems for efficient and automated service delivery. APIs facilitate the exchange of data and functionalities across diverse platforms, enhancing operational efficiency and user experience. However, this also introduces potential vulnerabilities that attackers can exploit to compromise system security, highlighting the importance of identifying and mitigating associated security risks. By examining the weaknesses inherent in these APIs using security open-intelligence catalogues like CWE and CAPEC and implementing controls from NIST SP 800-53, organizations can significantly enhance their security posture, safeguarding their data and systems against potential threats. However, this task is challenging due to evolving threats and vulnerabilities. Additionally, it is challenging to analyse threats given the large volume of traffic generated from API calls. This work contributes to tackling this challenge and makes a novel contribution to managing threats within system-to-system communication through API calls. It introduces an integrated architecture that combines deep-learning models, i.e., ANN and MLP, for effective threat detection from large API call datasets. The identified threats are analysed to determine suitable mitigations for improving overall resilience. Furthermore, this work introduces transparency obligation practices for the entire AI life cycle, from dataset preprocessing to model performance evaluation, including data and methodological transparency and SHapley Additive exPlanations (SHAP) analysis, so that AI models are understandable by all user groups. The proposed methodology was validated through an experiment using the Windows PE Malware API dataset, achieving an average detection accuracy of 88%. The outcomes from the experiments are summarized to provide a list of key features, such as FindResourceExA and NtClose, which are linked with potential weaknesses and related threats, in order to identify accurate control actions to manage the threats. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy (BLE) is a prominent short-range wireless communication protocol widely extended for communications and sensor systems in consumer electronics and industrial applications, ranging from manufacturing to retail and healthcare. The BLE protocol provides four generic access profile (GAP) roles when it is used in its low-energy version, i.e., ver. 4 and beyond. GAP roles control connections and allow BLE devices to interoperate each other. They are defined by the Bluetooth special interest group (SIG) and are primarily oriented to connect peripherals wirelessly to smartphones, laptops, and desktops. Consequently, the existing GAP roles have characteristics that do not fit well with vehicular communications in cooperative intelligent transport systems (C-ITS), where low-latency communications in high-density environments with stringent security demands are required. This work addresses this gap by developing two new GAP roles, defined at the application layer to meet the specific requirements of vehicular communications, and by providing a service application programming interface (API) for developers of vehicle-to-everything (V2X) applications. We have named this new approach ITS-BLE. These GAP roles are intended to facilitate BLE-based solutions for real-world scenarios on roads, such as detecting road traffic signs or exchanging information at toll booths. We have developed a prototype able to work indistinctly as a unidirectional or bidirectional communication device, depending on the use case. To solve security risks in the exchange of personal data, BLE data packets, here called packet data units (PDU), are encrypted or signed to guarantee either privacy when sharing sensitive data or authenticity when avoiding spoofing, respectively. Measurements taken and their later evaluation demonstrated the feasibility of a V2X BLE network consisting of picocells with a radius of about 200 m. [ABSTRACT FROM AUTHOR]

Summary: In this work, we address the problem of resource allocation in Internet of Everything (IoE)‐enabled software‐defined edge networks. In the existing literature, the researchers considered optimizing the performance of the software‐defined networking (SDN) platform using a single‐tier architecture, where the Internet of Things (IoT) devices are in the same tier. However, with the advent of edge computing, we can explore the two‐tier architecture of edge networks—local tier and edge tier—in the presence of SDN, which has not been explored. Hence, we propose an evolutionary game‐based resource allocation scheme for software‐defined edge networks. Additionally, we aim to optimize the QoS of the edge‐based IoE services while optimizing the throughput of the system. The IoT devices use the proposed scheme in the local tier to identify the optimized mapping to the SDN switches. On the other hand, in the edge tier, the proposed scheme aims to optimize the throughput while allocating the IoE service to the optimal subset of edge devices. We evaluated the performance of the proposed scheme using the Python3‐based Mininet platform in the presence of Ryu Controller and Open vSwitches. Ryu is an element‐based software defined networking framework, which offers software elements with well‐described API that enables developers to produce new network control functions. We observed that by using T‐RESIN, the network throughput increases by 27.98%–31.84% than using the existing schemes. [ABSTRACT FROM AUTHOR]

BRIDGES is a software framework for creating engaging assignments for required courses such as data structures and algorithms. It provides students with a simplified API that populates their own data structure implementations with live and real-world data, and provides the ability for students to easily visualize the data structures they create as part of routine classroom exercises. The objective is to use the infrastructure to promote a better understanding of the data structure and its underlying algorithms. This report describes the BRIDGES infrastructure and provides evaluation data collected over the first five years of the project. In the first 2 years, as we were developing the BRIDGES projects, our focus was on gathering data to assess whether the addition of the BRIDGES exercises had an effect on student retention of core concepts in data structures; and throughout the 5-year duration of the project, student interest and faculty feedback were collected online and anonymously. A mixed method design was used to evaluate the project impact. A quasiexperimental design compared student cohorts who were enrolled in comparable course sections that used BRIDGES with those that did not. Qualitative and quantitative measures were developed and used together with course grades and grade point averages. Interest and relevance in BRIDGES programming assignments was assessed with additional survey data from students and instructors. Results showed that students involved in BRIDGES projects demonstrated larger gains in knowledge of data structures compared to students enrolled in comparable course sections, as well as long-term benefits in their performance in four follow-on required courses. Survey responses indicated that some investment of time was needed to use BRIDGES, but the extra efforts were associated with several notable outcomes. Students and instructors had positive perceptions of the value of engaging in BRIDGES projects. BRIDGES can become a tool to get students more engaged in critical foundational courses, demonstrating relevance and context to today's computational challenges. [ABSTRACT FROM AUTHOR]

Digital transformation brings changes to the business world by increasing efficiency, convenience, security, certainty and operational speed. CV Elang Java Mandiri experienced the positive impact of this by creating software to increase operational efficiency. However, the use of direct communication between the desktop application and the database causes vulnerabilities. The lack of a bridge between the desktop application and the database also indicates a lack of flexibility when adding other applications. Ransomware attacks on desktop applications cause losses and limitations in development. This research focuses on backend updates that use API as an integrator with the API-first method. OpenAPI standards and OWASP security principles are used to increase resilience against security threats. These steps were tested with OWASP ZAP and http test. The goal is to provide solutions to company problems, meet the need for more secure applications, and make application development easier. [ABSTRACT FROM AUTHOR]

Rust is a system‐level programming language with advantages in memory safety. It ensures that any Rust programs without unsafe code should not incur undefined behaviours. However, unsafe code still plays an essential role in Rust to achieve low‐level control. Therefore, a major design pattern of Rust programs is interior unsafe, which wraps unsafe code as safe APIs and handles all undefined behaviours internally. Rust standard library already provides a rich set of safe APIs to facilitate Rust code development. Nevertheless, due to unfamiliarity with these APIs, developers may misuse unnecessary unsafe code and suffer memory‐safety risks. In this paper, we investigate an approach to mitigate replaceable unsafe code. We first analyse unsafe APIs of the Rust standard library and summarize their common usage patterns. Each pattern corresponds to one or several code samples in our knowledge base. Then, we develop an approach to automatically recognize the usage pattern and recommend corresponding code samples. Our approach leverages dataflow analysis to exclude impossible patterns and employs a BERT‐based machine learning model to find the most similar pattern among the rest. We have conducted evaluation experiments with 472 unsafe code snippets collected from GitHub projects and successfully recognized the pattern of 394 snippets. We hope our approach can assist developers in detecting unnecessary unsafe code and suggesting safe alternatives. [ABSTRACT FROM AUTHOR]

Early advances in quantum computing have provided new opportunities to tackle intricate problems in diverse areas such as cryptography, optimization, and simulation. However, current methodologies employed in quantum computing often require, among other things, a broad understanding of quantum hardware and low‐level programming languages, posing challenges to software developers in effectively creating and implementing quantum services. This study advocates the adoption of software engineering principles in quantum computing, thereby establishing a higher level of hardware abstraction that allows developers to focus on application development. With this proposal, developers can design and deploy quantum services with less effort, which is similar to the facilitation provided by service‐oriented computing for the development of conventional software services. This study introduces a continuous deployment strategy adapted to the development of quantum services that covers the creation and deployment of such services. For this purpose, an extension of the OpenAPI specification is proposed, which allows the generation of services that implement quantum algorithms. The proposal was validated through the creation of an application programming interface with diverse quantum algorithm implementations and evaluated through a survey of various developers and students who were introduced to the tool with positive results. [ABSTRACT FROM AUTHOR]

This study analyzed twitter trolling as a speech act of flaming. Trolling are deliberate disruptive practices of individuals or of particular group to sensationalize, commoditize, or intensify the reaction in online communication. Twitter API account was used to collect the tweets generated in Pakistan in English. The tweets were manually annotated with the help of a framework proposed by the Nitin, et al., 2011. and Lingam in UAM corpus tool. The finding revealed that tweets are trolled to flame by criticizing, name-calling, speculating, defaming, and degrading. The direct or intentional flaming showed derogatory behavior based on assumptions and aimed at inciting degenerative polarized responses. Moreover, this study also implies that there are serious moral and social implications of Twitter trolling. [ABSTRACT FROM AUTHOR]

Highlighting the importance of environmental monitoring data and its management, and the usage of standards for providing interoperability to geospatial information, we present and document the implementation of ROSE-API (Reusable Open Source Environmental-Data-Management OGC API), an OGC API-compliant web service for exposing feature data and georeferenced timeseries, as the ones usually generated by environmental monitoring networks. Multiple OGC API standards that align with the goal of exposing environmental data were implemented, in particular OGC API - Features, Environmental Data Retrieval (EDR), and Processes. With interoperability and flexibility in mind, ROSE-API allows discovery and processing capabilities for environmental monitoring data, exposing it in standardised formats and structures suitable for modern internet usage, allowing spatial and non-spatial filtering, and providing server-side processing capabilities. We tested our implementation with 13 years of air quality observations, consisting of more than 50 million data points, to address its compliance with the implemented OGC APIs, performance metrics, and processing capabilities. [ABSTRACT FROM AUTHOR]

Polarizable continuum solvation models are popular in both, quantum chemistry and in biophysics, though typically with different requirements for the numerical methods. However, the recent trend of multiscale modeling can be expected to blur field‐specific differences. In this regard, numerical methods based on domain decomposition (dd) have been demonstrated to be sufficiently flexible to be applied all across these levels of theory while remaining systematically accurate and efficient. In this contribution, we present ddX, an open‐source implementation of dd‐methods for various solvation models, which features a uniform interface with classical as well as quantum descriptions of the solute, or any hybrid versions thereof. We explain the key concepts of the library design and its application program interface, and demonstrate the use of ddX for integrating into standard chemistry packages. Numerical tests illustrate the performance of ddX and its interfaces. This article is categorized under:Software > Quantum ChemistrySoftware > Simulation Methods [ABSTRACT FROM AUTHOR]

Every industry whether it is Oil & gas, Manufacturing, Life sciences, Financial services etc. are embracing digital technologies to remain competitive as well better serve the customers. Life Insurance industry cannot be lagging in this journey. The Life insurance industry future depends on exploring new models of doing business to ensure its relevance. To succeed in this competitive insurance market, companies will require speed, agility, efficiency, faster policy issuance, claim processing and ease of doing business across with its channel partners. To serve better, Insurance companies should be going digital way. This article speaks about the key drivers for digital transformation in Life insurance industry and which areas insurance company should focus on to stay viable and grow in this age of digitalization. [ABSTRACT FROM AUTHOR]

Web-based systems are used extensively in Korea because web standards have been adapted by the law (e.g., Electronic Government Act). Users can easily access web-based systems if they are connected to the Internet. However, distinguishing between malicious and benign access is very difficult and many potential vulnerabilities exist. In this study, we attempt to leak the information of other users without permission using a non-encrypted API and web source code analysis in a college web-based system. An experiment demonstrates that the information (e.g., other students' course grades) can be leaked and abnormal data can be embedded in the request. In addition, we discuss methods for preventing such vulnerability attacks. [ABSTRACT FROM AUTHOR]

Task-oriented dialogue systems (TODs) enable users to complete specific goals and are widely used in practice. Although existing models have achieved delightful performance for single-domain dialogues, scalability to new domains is far from well explored. Traditional dialogue systems rely on domain-specific information like dialogue state and database (DB), which limits the scalability of such systems. In this paper, we propose a Scalable Task-Oriented Dialogue modeling framework (STOD). Instead of labeling multiple dialogue components, which have been adopted by previous work, we only predict structured API queries to interact with DB and generate responses based on the complete DB results. Further, we construct a new API-schema-based TOD dataset MultiWOZ-API with API query and DB result annotation based on MultiWOZ 2.1. We then propose MSTOD and CSTOD for multi-domain and cross-domain TOD systems, respectively. We perform extensive qualitative experiments to verify the effectiveness of our proposed framework. We find the following. (1) Scalability across multiple domains: MSTOD achieves 2% improvements than the previous state-of-the-art in the multi-domain TOD. (2) Scalability to new domains: our framework enables satisfying generalization capability to new domains, a significant margin of 10% to existing baselines. [ABSTRACT FROM AUTHOR]

Aim: To test the association between sociodemographic and social characteristics with COVID-19 cases and deaths in small and large Brazilian cities. Methods: This ecological study included COVID-19 data available in State Health Secretaries (managed by brasil.io API) and three national databases (IBGE, DATASUS and Embrapa). Temporal spread of COVID-19 in Brazil during the first year considered as outcome: a) days until 1st case in each city since 1st in the country; b) days until 1,000 cases/100,000 inhabitants since 1st case in each city; c) days until 1st death until 50 deaths/100,000 inhabitants. Covariates included geographic region, city social and environmental characteristics, housing conditions, job characteristics, socioeconomic and inequalities characteristics, and health services and coverage. The analysis were stratified by city size into small (<100,000 inhabitants) and large cities (≥100,00 inhabitants). Multiple linear regressions were performed to test associations of all covariates to adjust to potential confounders. Results: In small cities, the first cases were reported after 82.2 days and 1,000 cases/100,000 were reported after 117.8 days, whereas in large cities these milestones were reported after 32.1 and 127.7 days, respectively. For first death, small and large cities took 121.6 and 36.0 days, respectively. However, small cities were associated with more vulnerability factors to first case arrival in 1,000 cases/100,000 inhabitants, first death and 50 deaths/100,000 inhabitants. North and Northeast regions positively associated with faster COVID-19 incidence, whereas South and Southeast were least. Conclusion: Social and built environment characteristics and inequalities were associated with COVID-19 cases spread and mortality incidence in Brazilian cities. [ABSTRACT FROM AUTHOR]

The digital transformation of architecture, engineering, and construction (AEC) is being hailed as a panacea for the sectors' problems, with major actors in government and business promoting it as a benevolent harbinger of greater efficiency, productivity, better quality, and environmental performance. However, advancing digitalization in AEC is intertwined with a distinct political-economic logic that involves new constraints, dependencies, and a reconfiguration of power relations. Specifically, Building Information Modeling (BIM) as a digital convergence of three-dimensional building models, databases, and interfaces for multidisciplinary collaboration operates as a runway technology for digitalization and an entry point for platformization impelled by global software giants, such as this article's empirical case of software company Autodesk and its BIM product portfolio shows. BIM's linkage of governmental policy strategies with the techno-economic platform logic of software subscription models, open application programming interfaces, and cloud-enabled collaboration ensures management and control of construction data and software interoperability. At the same time, BIM's techno-economic and political configuration facilitates a near-monopolistic structure in the global architecture and construction software market. It thus grants one hegemonic company immense power to define what formats, standards, and data for building models and design processes are being used in creating the built environment. The expansive, intersecting logics of assetization, platformization, and datafication have been taking hold in the AEC sector, raising questions about their stratifying effects in the built environment and society. [ABSTRACT FROM AUTHOR]

This study examines the dynamic interplay between platform providers and complementors in the context of digital ecosystems, focusing on the complementary factors of affordance, autonomy, and super-modularity. Using national survey data from the Korean digital industry, the study applied multivariate ordered probit and k-mode clustering models to analyze what determines these factors and how these factors are interrelated from the perspective of platform providers and complementors, respectively. The results indicate that platform providers with open APIs promote affordance, but providing an SDK inhibits affordance. In terms of complementors, choosing a platform providing APIs increases super-modularity. And affordance increases when using the platform for logistics and new product development. In addition, we found that affordance and autonomy have a trade-off relationship from the perspective of both platform providers and complementors. Finally, we classified platforms and complementors into subgroups with respect to affordance, autonomy, and super-modularity using cluster analysis and found that the size of a complementor's firm, such as revenue and number of employees, influences which platform it chooses. Conversely, the size of a platform provider also influences how much autonomy and collaboration it offers. This study contributes to the understanding of digital platform ecosystems and provides insights for practitioners on how to leverage platform dynamics to enhance competitive advantage. [ABSTRACT FROM AUTHOR]

Understanding the behaviour of a system's API can be hard. Giving users access to relevant examples of how an API behaves has been shown to make this easier for them. In addition, such examples can be used to verify expected behaviour or identify unwanted behaviours. Methods for automatically generating examples have existed for a long time. However, state-of-the-art methods rely on either white-box information, such as source code, or on formal specifications of the system behaviour. But what if you do not have access to either? This may be the case, for example, when interacting with a third-party API. In this paper, we present an approach to automatically generate relevant examples of behaviours of an API, without requiring either source code or a formal specification of behaviour. Evaluation on an industry-grade REST API shows that our method can produce small and relevant examples that can help engineers to understand the system under exploration. [ABSTRACT FROM AUTHOR]

Weather forecasting is one of the most scientifically and technologically challenging problems around the world in the last century. To make an accurate prediction is indeed, one of the major challenges that meteorologists are facing all over the world. To predict the conditions of the atmosphere for a given location, Weather Forecasting is used. Weather forecasting is made by collecting numerous data predicted by very proper understanding of the collected data. Weather simply refers to the condition of air on the earth at given place and time. It is a continuous, data-intensive, multidimensional, dynamic and chaotic process. These processes make weather forecasting a formidable challenge. [ABSTRACT FROM AUTHOR]

Millions of users nowadays rely on their smartphones to process sensitive data through apps from various vendors and sources. Therefore, it is vital to assess these apps for security vulnerabilities and privacy violations. Information such as to which server an app connects through which protocol, and which algorithm it applies for encryption, are usually encoded as variable values and arguments of API calls. However, extracting these values from an app is not trivial. The source code of an app is usually not available, and manual reverse engineering is cumbersome with binary sizes in the tens of megabytes. Current automated tools, however, cannot retrieve values that are computed at runtime through complex transformations. In this article, we present ValDroid, a novel static analysis tool for automatically extracting the set of possible values for a given variable at a given statement in the Dalvik byte code of an Android app. We evaluate ValDroid against existing approaches (JSA, Violist, DroidRA, Harvester, BlueSeal, StringHound, IC3, and COAL) on benchmarks and 794 real-world apps. ValDroid greatly outperforms existing tools. It provides an average F1 score of more than 90%, while only requiring 0.1 s per value on average. For many data types including Network Connections and Dynamic Code Loading, its recall is more than twice the recall of the best existing approaches. [ABSTRACT FROM AUTHOR]

The article explores the governance considerations for open foundation models like GPT-4 and Llama 3.1, which are driving AI innovation and attracting significant investment. While these models have the potential to benefit society by promoting competition and distributing power, there are concerns about risks such as malicious use and the spread of disinformation. The article suggests that policymakers should carefully consider the impact of regulation on the innovation ecosystem surrounding open foundation models. It also discusses the potential risks associated with open foundation models in AI, including voice-cloning scams, NCII, and CSAM. The article emphasizes the importance of considering the interests of the open foundation model community when crafting regulations. [Extracted from the article]

In the context of platforms, an open architecture is instrumental in enabling innovation by complementors. But as complementors increasingly deplete the innovation opportunities that the platform architecture affords, the platform architecture must evolve to reinvigorate the platform's generative capacity. This article underscores the role of complementors in the process of platform architecture evolution by introducing the notion of architectural generativity. Architectural generativity involves actively soliciting and selectively incorporating contributions from complementors to help evolve the platform in unforeseen ways. In the case of the Mozilla Firefox web browser platform, complementors contributed new ways to facilitate access to the platform's technological components and suggested better ways to control the platform and its architecture. [ABSTRACT FROM AUTHOR]

Marketing scholars increasingly use web scraping and application programming interfaces (APIs) to collect data from the internet. Yet, despite the widespread use of such web data, the idiosyncratic and sometimes insidious challenges in its collection have received limited attention. How can researchers ensure that the data sets generated via web scraping and APIs are valid? While existing resources emphasize technical details of extracting web data, the authors propose a novel methodological framework focused on enhancing its validity. In particular, the framework highlights how addressing validity concerns requires the joint consideration of idiosyncratic technical and legal/ethical questions along the three stages of collecting web data: selecting data sources, designing the data collection, and extracting the data. The authors further review more than 300 articles using web data published in the top five marketing journals and offer a typology of how web data have advanced marketing thought. The article concludes with directions for future research to identify promising web data sources and embrace novel approaches for using web data to capture and describe evolving marketplace realities. [ABSTRACT FROM AUTHOR]

Test coverage is a critical aspect of the software development process, aiming for overall confidence in the product. When considering cloud-native systems, testing becomes complex, as it becomes necessary to deal with multiple distributed microservices that are developed by different teams and may change quite rapidly. In such a dynamic environment, it is important to track test coverage. This is especially relevant for end-to-end (E2E) and API testing, as these might be developed by teams distinct from microservice developers. Moreover, indirection exists in E2E, where the testers may see the user interface but not know how comprehensive the test suits are. To ensure confidence in health checks in the system, mechanisms and instruments are needed to indicate the test coverage level. Unfortunately, there is a lack of such mechanisms for cloud-native systems. This manuscript introduces test coverage metrics for evaluating the extent of E2E and API test suite coverage for microservice endpoints. It elaborates on automating the calculation of these metrics with access to microservice codebases and system testing traces, delves into the process, and offers feedback with a visual perspective, emphasizing test coverage across microservices. To demonstrate the viability of the proposed approach, we implement a proof-of-concept tool and perform a case study on a well-established system benchmark assessing existing E2E and API test suites with regard to test coverage using the proposed endpoint metrics. The results of endpoint coverage reflect the diverse perspectives of both testing approaches. API testing achieved 91.98% coverage in the benchmark, whereas E2E testing achieved 45.42%. Combining both coverage results yielded a slight increase to approximately 92.36%, attributed to a few endpoints tested exclusively through one testing approach, not covered by the other. [ABSTRACT FROM AUTHOR]