Dynamic verification of mashups of service-oriented things through a mediation platform

The new Internet is evolving into the vision of the Internet of Things, where physical world entities are integrated into virtual world things. Things are expected to become active participants in business, information and social processes. Then, the Internet of Things could benefit from the Web Service architecture like today’s Web does; so Future service-oriented Internet things will offer their functionality via service-enabled interfaces. As demonstrated in previous work, there is a need of considering the behaviour of things to develop applications in a more rigorous way. We proposed a lightweight model for representing such behaviour based on the service-oriented paradigm and extending the standard DPWS profile to specify the (partial) order with which things can receive messages. To check whether a mashup of things respects the behaviour, specified at design-time, of composed things, we proposed a static verification. However, at run-time a thing may change its behaviour or receive requests from instances of different mashups. Then, it is required to check and detect dynamically possible invalid invocations provoked by the behaviour’s changes. In this work, we extend our static verification with an approach based on mediation techniques and complex event processing to detect and inhibit invalid invocations, checking that things only receive requests compatible with their behaviour. The solution automatically generates the required elements to perform run-time validation of invocations, and it may be extended to validate other issues. Here, we have also dealt with quality of service and temporal restrictions

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

FinGPT: Open-Source Financial Large Language Models

Large language models (LLMs) have shown the potential of revolutionizing natural language processing tasks in diverse domains, sparking great interest in finance. Accessing high-quality financial data is the first challenge for financial LLMs (FinLLMs). While proprietary models like BloombergGPT have taken advantage of their unique data accumulation, such privileged access calls for an open-source alternative to democratize Internet-scale financial data. In this paper, we present an open-source large language model, FinGPT, for the finance sector. Unlike proprietary models, FinGPT takes a data-centric approach, providing researchers and practitioners with accessible and transparent resources to develop their FinLLMs. We highlight the importance of an automatic data curation pipeline and the lightweight low-rank adaptation technique in building FinGPT. Furthermore, we showcase several potential applications as stepping stones for users, such as robo-advising, algorithmic trading, and low-code development. Through collaborative efforts within the open-source AI4Finance community, FinGPT aims to stimulate innovation, democratize FinLLMs, and unlock new opportunities in open finance. Two associated code repos are \url{https://github.com/AI4Finance-Foundation/FinGPT} and \url{https://github.com/AI4Finance-Foundation/FinNLP

System Abstractions for Scalable Application Development at the Edge

Recent years have witnessed an explosive growth of Internet of Things (IoT) devices, which collect or generate huge amounts of data. Given diverse device capabilities and application requirements, data processing takes place across a range of settings, from on-device to a nearby edge server/cloud and remote cloud. Consequently, edge-cloud coordination has been studied extensively from the perspectives of job placement, scheduling and joint optimization. Typical approaches focus on performance optimization for individual applications. This often requires domain knowledge of the applications, but also leads to application-specific solutions. Application development and deployment over diverse scenarios thus incur repetitive manual efforts. There are two overarching challenges to provide system-level support for application development at the edge. First, there is inherent heterogeneity at the device hardware level. The execution settings may range from a small cluster as an edge cloud to on-device inference on embedded devices, differing in hardware capability and programming environments. Further, application performance requirements vary significantly, making it even more difficult to map different applications to already heterogeneous hardware. Second, there are trends towards incorporating edge and cloud and multi-modal data. Together, these add further dimensions to the design space and increase the complexity significantly. In this thesis, we propose a novel framework to simplify application development and deployment over a continuum of edge to cloud. Our framework provides key connections between different dimensions of design considerations, corresponding to the application abstraction, data abstraction and resource management abstraction respectively. First, our framework masks hardware heterogeneity with abstract resource types through containerization, and abstracts away the application processing pipelines into generic flow graphs. Further, our framework further supports a notion of degradable computing for application scenarios at the edge that are driven by multimodal sensory input. Next, as video analytics is the killer app of edge computing, we include a generic data management service between video query systems and a video store to organize video data at the edge. We propose a video data unit abstraction based on a notion of distance between objects in the video, quantifying the semantic similarity among video data. Last, considering concurrent application execution, our framework supports multi-application offloading with device-centric control, with a userspace scheduler service that wraps over the operating system scheduler

Software Supply Chain Development and Application

Motivation: Free Libre Open Source Software (FLOSS) has become a critical componentin numerous devices and applications. Despite its importance, it is not clear why FLOSS ecosystem works so well or if it may cease to function. Majority of existing research is focusedon studying a specific software project or a portion of an ecosystem, but FLOSS has not been investigated in its entirety. Such view is necessary because of the deep and complex technical and social dependencies that go beyond the core of an individual ecosystem and tight inter-dependencies among ecosystems within FLOSS.Aim: We, therefore, aim to discover underlying relations within and across FLOSS projects and developers in open source community, mitigate potential risks induced by the lack of such knowledge and enable systematic analysis over entire open source community through the lens of supply chain (SC).Method: We utilize concepts from an area of supply chains to model risks of FLOSS ecosystem. FLOSS, due to the distributed decision making of software developers, technical dependencies, and copying of the code, has similarities to traditional supply chain. Unlike in traditional supply chain, where data is proprietary and distributed among players, we aim to measure open-source software supply chain (OSSC) by operationalizing supply chain concept in software domain using traces reconstructed from version control data.Results: We create a very large and frequently updated collection of version control data in the entire FLOSS ecosystems named World of Code (WoC), that can completely cross-reference authors, projects, commits, blobs, dependencies, and history of the FLOSS ecosystems, and provide capabilities to efficiently correct, augment, query, and analyze that data. Various researches and applications (e.g., software technology adoption investigation) have been successfully implemented by leveraging the combination of WoC and OSSC.Implications: With a SC perspective in FLOSS development and the increased visibility and transparency in OSSC, our work provides potential opportunities for researchers to conduct wider and deeper studies on OSS over entire FLOSS community, for developers to build more robust software and for students to learn technologies more efficiently and improve programming skills

Modeling Users Feedback Using Bayesian Methods for Data-Driven Requirements Engineering

Data-driven requirements engineering represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. App developers now receive abundant user feedback from user comments in app stores and social media, i.e., explicit feedback, to feedback from usage data and system logs, i.e, implicit feedback. In this dissertation, we describe two novel Bayesian approaches that utilize the available user\u27s to support requirements decisions and activities in the context of applications delivered through software marketplaces (web and mobile). In the first part, we propose to exploit implicit user feedback in the form of usage data to support requirements prioritization and validation. We formulate the problem as a popularity prediction problem and present a novel Bayesian model that is highly interpretable and offers early-on insights that can be used to support requirements decisions. Experimental results demonstrate that the proposed approach achieves high prediction accuracy and outperforms competitive models. In the second part, we discuss the limitations of previous approaches that use explicit user feedback for requirements extraction, and alternatively, propose a novel Bayesian approach that can address those limitations and offer a more efficient and maintainable framework. The proposed approach (1) simplifies the pipeline by accomplishing the classification and summarization tasks using a single model, (2) replaces manual steps in the pipeline with unsupervised alternatives that can accomplish the same task, and (3) offers an alternative way to extract requirements using example-based summaries that retains context. Experimental results demonstrate that the proposed approach achieves equal or better classification accuracy and outperforms competitive models in terms of summarization accuracy. Specifically, we show that the proposed approach can capture 91.3% of the discussed requirement with only 19% of the dataset, i.e., reducing the human effort needed to extract the requirements by 80%

Measuring Success for a Future Vision: Defining Impact in Science Gateways/Virtual Research Environments

Scholars worldwide leverage science gateways/VREs for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this paper, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of e.g., their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. Correspondingly, we present a number of technology advances that could be incorporated in next-generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the Science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest Group (VRE-IG) of the Research Data Alliance. Thus, community-driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc