A community needs assessment is a tool used by non-profits and government agencies to quantify the strengths and issues of a community, allowing them to allocate their resources better. Such approaches are transitioning towards leveraging social media conversations to analyze the needs of communities and the assets already present within them. However, manual analysis of exponentially increasing social media conversations is challenging. There is a gap in the present literature in computationally analyzing how community members discuss the strengths and needs of the community. To address this gap, we introduce the task of identifying, extracting, and categorizing community needs and assets from conversational data using sophisticated natural language processing methods. To facilitate this task, we introduce the first dataset about community needs and assets consisting of 3,511 conversations from Reddit, annotated using crowdsourced workers. Using this dataset, we evaluate an utterance-level classification model compared to sentiment classification and a popular large language model (in a zero-shot setting), where we find that our model outperforms both baselines at an F1 score of 94% compared to 49% and 61% respectively. Furthermore, we observe through our study that conversations about needs have negative sentiments and emotions, while conversations about assets focus on location and entities.

Current research concentrates on studying discussions on social media related to structural failures to improve disaster response strategies. However, detecting social web posts discussing concerns about anticipatory failures is under-explored. If such concerns are channeled to the appropriate authorities, it can aid in the prevention and mitigation of potential infrastructural failures. In this paper, we develop an infrastructure ombudsman -- that automatically detects specific infrastructure concerns. Our work considers several recent structural failures in the US. We present a first-of-its-kind dataset of 2,662 social web instances for this novel task mined from Reddit and YouTube.

Conversational assistive robots can aid people, especially those with cognitive impairments, to accomplish various tasks such as cooking meals, performing exercises, or operating machines. However, to interact with people effectively, robots must recognize human plans and goals from noisy observations of human actions, even when the user acts sub-optimally. Previous works on Plan and Goal Recognition (PGR) as planning have used hierarchical task networks (HTN) to model the actor/human. However, these techniques are insufficient as they do not have user engagement via natural modes of interaction such as language. Moreover, they have no mechanisms to let users, especially those with cognitive impairments, know of a deviation from their original plan or about any sub-optimal actions taken towards their goal. We propose a novel framework for plan and goal recognition in partially observable domains—Dialogue for Goal Recognition (D4GR) enabling a robot to rectify its belief in human progress by asking clarification questions about noisy sensor data and sub-optimal human actions. We evaluate the performance of D4GR over two simulated domains—kitchen and blocks domain. With language feedback and the world state information in a hierarchical task model, we show that D4GR framework for the highest sensor noise performs 1% better than HTN in goal accuracy in both domains. For plan accuracy, D4GR outperforms by 4% in the kitchen domain and 2% in the blocks domain in comparison to HTN. The ALWAYS-ASK oracle outperforms our policy by 3% in goal recognition and 7% in plan recognition. D4GR does so by asking 68% fewer questions than an oracle baseline. We also demonstrate a real-world robot scenario in the kitchen domain, validating the improved plan and goal recognition of D4GR in a realistic setting.

This study explores the benefits and challenges of using robotic process automation (RPA) and collaborative robots (CoBot) in transport management. The combination of RPA and CoBot can streamline processes, minimize errors, and boost productivity. However, the implementation of such technology comes with challenges and costs [1].This study aims to provide valuable insights into the role of RPA and CoBot in transport management, adding to the growing body of research on the use of automation in logistics and supply chain management [2]. Our analysis indicates that RPA and CoBot can revolutionize transport management, but their implementation requires careful consideration of factors such as security and investment. The study fills a gap in the literature by providing an in-depth examination of the benefits and challenges of using RPA and CoBot in transport management, offering practical recommendations for organizations considering implementing such technology.

Service composition algorithms support the construction of complex applications by combining various web services to fulfill diverse functional and Quality of Service (QoS) requirements. Moreover, composition algorithms must fulfill diverse user requirements while adhering to constraints such as limited computational resources. Recent research has demonstrated that using online learning to select different algorithms for specific tasks of a problem domain outperforms approaches that use a single algorithm for all tasks, in terms of computational resource usage and solution quality. Problematically, existing work in service composition does not leverage these advances, leading to multiple inefficient compositions. To address these challenges, we propose online composition algorithm selection using contextual multi-armed bandits to select an algorithm for each composition task at runtime. Our evaluations demonstrate the benefits of our approach by reducing time and memory usage by up to 54.2% and 15.5% while fulfilling QoS requirements, compared to using a single composition algorithm for all tasks.

In service composition, complex applications are built by combining web services to fulfill user Quality of Service (QoS) and business requirements. To meet these requirements, applications are composed by evaluating all possible web service combinations using search algorithms. These algorithms need to be accurate and inexpensive to evaluate a large number of possible service combinations and services' fluctuating QoS attributes while meeting the constraints of limited computational resources. Recent research has shown that different search algorithms can outperform others on specific instances of a problem domain, in terms of solution quality and computational resource usage. Problematically, current service composition approaches ignore this property, leading to inefficient compositions. To address these limitations, we propose a composition algorithm selection framework which selects an algorithm per composition task at runtime, R-CASS. Our evaluations demonstrate that R-CASS leads to more efficient compositions, reducing composition time by 55.1% and memory by 37.5%.

As cloud computing has become popular and the number of cloud providers has proliferated over time, the first barrier to cloud users will be how to accurately estimate performance capabilities of many different clouds and then, select a right one for given complex workload based on estimates. Such cloud capability estimation and selection can be a big challenge since most clouds can be considered as black-boxes to cloud users by abstracting underlying infrastructures and technologies. This paper describes a cloud recommender system to recommend an optimal cloud configuration to users based on accurate estimates. To achieve this, our system generates the capability vector that consists of relative performance scores of resource types (e.g., CPU, memory, and disk) estimated for given user workload using benchmarks. Then, a search algorithm has been developed to identify an optimal cloud configuration based on these collected capability vectors. Experiments show our approach accurately estimate the performance capability (less than 10% error) while scalable in large search space.

The proliferation of cloud computing can imply a barrier to cloud users. When deploying their complex workloads into clouds, cloud users are typically overwhelmed by too many technical choices. Moreover, underlying technologies and pricing mechanisms of clouds vary and are not transparent to them. Consequently, it is hard for cloud users to capture the monetary and performance implications of their workload deployments. This paper introduces a cloud recommendation platform, referred to as Cloud Advisor. It allows cloud users to explore various cloud configurations recommended based on user preferences such as budget, performance expectation, and energy saving for given workload. Then, it allows cloud users to compare offered price and performance with other clouds' offerings for the workload. By providing transparent comparisons, it can also support cloud provider to develop a competitive pricing strategy such as price reduction driven by energy efficiency. We have applied the proposed platform for recommendation from a real data center and some external clouds.

Optimizing ordered throughput not only improves the system efficiency but also makes cloud bursting transparent to the user. This is critical from the perspective of user fairness in customer-facing systems, correctness in stream processing systems, and so on. In this paper, we consider optimizing ordered throughput for near real-time, data-intensive, independent computations using cloud bursting. Intercloud computation of data-intensive applications is a challenge due to large data transfer requirements, low intercloud bandwidth, and best-effort traffic on the Internet. The system model we consider is comprised of two processing stages. The first stage uses cloud bursting opportunistically for parallel processing, while the second stage (sequential) expects the output of the first stage to be in the same order as the arrival sequence. We propose three scheduling heuristics as part of an autonomic cloud bursting approach that adapt to changing workload characteristics, variation in bandwidth, and available resources to optimize ordered throughput. We also characterize the operational regimes for cloud bursting as stabilization mode versus acceleration mode, depending on the workload characteristics like the size of data to be transferred for a given compute load. The operational regime characterization helps in deciding how many instances can be optimally utilized in the external cloud.

Business Process Management (BPM) software provides visibility into business processes in organizations of all sizes and helps increase process efficiency continuously. However, the time and effort involved in modeling, deploying and executing a business process is tremendous and as a result organizations struggle to agilely adapt business processes to dynamic business requirements. On the other hand, the growing popularity of cloud computing poses opportunities and challenges on how business processes can leverage resource outsourcing and elasticity. In light of the above, this paper presents a business process management platform that assists business analysts lacking necessary programming expertise by automating manual steps and providing guidance and recommendations to quickly and efficiently design, implement, deploy and execute business processes in a hybrid cloud environment.

Ensuring the efficient and robust operation of distributed computational infrastructures is critical, given that their scale and overall complexity is growing at an alarming rate and that their management is rapidly exceeding human capability. Clustering analysis can be used to find patterns and trends in system operational data, as well as highlight deviations from these patterns. Such analysis can be essential for verifying the correctness and efficiency of the operation of the system, as well as for discovering specific situations of interest, such as anomalies or faults, that require appropriate management actions. This work analyzes the automated application of clustering for online system management, from the point of view of the suitability of different clustering approaches for the online analysis of system data in a distributed environment, with minimal prior knowledge and within a timeframe that allows the timely interpretation of and response to clustering results. For this purpose, we evaluate DOC (Decentralized Online Clustering), a clustering algorithm designed to support data analysis for autonomic management, and compare it to existing and widely used clustering algorithms. The comparative evaluations will show that DOC achieves a good balance in the trade-offs inherent in the challenges for this type of online management.

In the fall 2010 issue of the AI Magazine, we reported the design, implementation and evaluation of a knowledge acquisition system called AURA. AURA enables domain experts in Physics, Chemistry and Biology to author their knowledge, and a different set of experts to pose questions against that knowledge. The evaluation results previously reported were from 50 pages each from science textbooks in Physics, Chemistry and Biology. The results were most promising for Biology. Based on those results we undertook a content building effort to capture knowledge from approximately 315 pages (or 20 chapters) of the same Biology textbook [2] and incorporated the resulting content in the electronic version of that book. In this demo/poster session, we will demonstrate the biology knowledge base (KB) created using AURA, the electronic textbook application Inquire, and discuss the knowledge engineering process we used to construct the KB.

In present works authors have developed a computerized classification procedure for tumor mass in breasts using digital mammogram. The process implements genetic algorithm and hybrid neuro-fuzzy approaches to classify tumor masses into benign and malignant group in order to assist the physicians for treatment planning. The classification process is based on accurate analysis of shape and margin of tumor mass appearing in breast. The shape features using Fourier descriptors introduce a large number of feature vectors. Thus, to classify different boundaries, a standard multilayer preceptor needs large number of inputs. Simultaneously, to train the network, a large number of training cycles and huge memory are also required. It is obvious that a complicated structure invites the problem of over learning and misclassification. In proposed methodology genetic algorithm (GA) has been used for the searching of effective input feature vectors. Adaptive neuro-fuzzy model has been used for final classification of different boundaries of tumor masses. The proposed technique is an innovative soft computing approach that removes the limitation of conventional neural networks and indicates a promising direction of adaptation in a changing environment. The classification system utilizes a Euclidean distance function to detect the belongingness of masses in benign and in malignant classes along with degree of benignancy/malignancy. Presently 200 digitized mammograms from MIAS and other databases have been considered for the experiment and which have shown an average of approximately 86% correct classification as compared with clinical data with a highest rate of 88.9%.

Parallel computing on volatile distributed resources requires schedulers that consider job and resource characteristics. We study unconventional computing environments containing devices spread throughout a single large organization. The devices are not necessarily typical general purpose machines; instead, they could be processors dedicated to special purpose tasks (for example printing and document processing), but capable of being leveraged for distributed computations. Harvesting their idle cycles can simultaneously help resources cooperate to perform their primary task and enable additional functionality and services. A new burstiness metric characterizes the volatility of the high-priority native tasks. A burstiness-aware scheduling heuristic opportunistically introduces grid jobs (a lower priority workload class) to avoid the higher-priority native applications, and effectively harvests idle cycles. Simulations based on real workload traces indicate that this approach improves makespan by an average of 18.3% over random scheduling, and comes within 7.6% of the theoretical upper bound.

Peer-to-peer (P2P) file sharing systems are becoming increasingly popular due to their flexibility and scalability. We propose a new model to design file replication schemes for P2P file sharing systems. The model introduces expected costs for serving user requests for the files which are computed from node up/down statistics. Based on the model we introduce and develop several methods to determine the sets of nodes to store copies of the files in order to optimize certain performance metrics (e.g., maximize the system hit rate, minimize the total expected cost). We verify the effectiveness of the file replication schemes via simulation. We also outline a framework to implement the file replication schemes for P2P file sharing systems in a distributed and adaptive manner. The framework scales to a large number of nodes and files and can handle user request pattern change via file migration.

Weyuker's Property 9 has received a mixed response regarding its applicability to object oriented software metrics. Contrary to past beliefs, the relevance of this property to object oriented systems is brought out. In support of the new argument, counterexamples to earlier claims are formulated and two new metrics highlighting a notion of complexity that is capturable through Property 9 are also presented.