Reviewer Matchmaking (RM) is a pivotal process in academic publishing that aligns manuscripts with appropriate reviewers based on their expertise and prior publications. The demand for an automated RM system has escalated with the significant surge in submissions over the past decade. State-of-the-art (SOTA) RM models are document-representation-based (DR-RM) and match the manuscript and reviewer's past publication using a similarity method defined on a high-dimensional vector space. However, they are far from accurate despite their large-scale usage. In this paper, we establish that conventional RM evaluation measures are unreliable and instead emphasize that standard correlation measures are adequate. For the first time, we compare the performance of six SOTA DR-RM models with those of fourteen SOTA Key-phrase Extraction-based RM (KPE-RM) models - an alternate unexplored approach. We observe that KPE-RM models show comparable results in many cases, with the new best model being PatternRank-RM - a KPE-RM model beating the best DR-RM model SPECTER2-RM (Pearson: 0.004+, Spearman: 0.006+, Kendall: 0.043+). We conclude that KPE-RM models must be contextualized to the RM task and cannot be used as plug-n-play.

Large Language Models (LLMs) have succeeded considerably in In-Context-Learning (ICL) based summarization.However, saliency is subject to the users' specific preference histories.Hence, we need reliable In-Context Personalization Learning (ICPL) capabilities within such LLMs.For any arbitrary LLM to exhibit ICPL, it needs to have the ability to discern contrast in user profiles.A recent study proposed a measure for degree-ofpersonalization called EGISES for the first time.EGISES measures a model's responsiveness to user profile differences.However, it cannot test if a model utilizes all three types of cues provided in ICPL prompts: (i) example summaries, (ii) user's reading histories, and (iii) contrast in user profiles.To address this, we propose the iCOPERNICUS framework, a novel In-Context Personalization Learning Scrutiny of Summarization capability in LLMs that uses EGISES as a comparative measure.As a casestudy, we evaluate 17 state-of-the-art LLMs based on their reported ICL performances and observe that 15 models' ICPL degrades (min: 1.6%↓; max: 3.6%↓) when probed with richer prompts, thereby showing lack of true ICPL.

RTs generally possess a fixed syntactic structure and comprise pre-defined slots, and requirements written in the format of RTs must conform with the template structure. If the requirements do not conform to the RT, manually rewriting them to adhere to the RTs structure is tedious. In this paper, we develop the AutoReco tool for the automated recommendation of functional requirements for non-conformance with requirement templates (RTs). Our preliminary results on nine case studies show an accuracy of 83.9% for providing recommendations to non-conformant requirements with RTs. Demonstration video: https://youtu.be/OSX71cP-oXw

Document similarity is the problem of estimating the degree to which a given pair of documents has similar semantic content. An accurate document similarity measure can improve several enterprise relevant tasks such as document clustering, text mining, and question-answering. In this paper, we show that a document's thematic flow, which is often disregarded by bag-of-word techniques, is pivotal in estimating their similarity. To this end, we propose a novel semantic document similarity framework, called SimDoc. We model documents as topic-sequences, where topics represent latent generative clusters of related words. Then, we use a sequence alignment algorithm to estimate their semantic similarity. We further conceptualize a novel mechanism to compute topic-topic similarity to fine tune our system. In our experiments, we show that SimDoc outperforms many contemporary bag-of-words techniques in accurately computing document similarity, and on practical applications such as document clustering.

Angry Birds is a popular video game in which a set of birds has to perform sling shots (bird shots) so as to kill pigs that are protected by a structure composed of different building blocks. The fewer birds we use and the more blocks we destroy, the higher the score we achieve. AIBirds competition is an AI challenge where an intelligent bot has to be developed that plays the game without human intervention. In this paper, we describe the approach implemented in the bot, s-birds Avengers, that participated in ECAI AIBirds 2014. Heuristic techniques were designed to analyze unseen structures using various structural parameters and then to discover their vulnerable points using prior parameter learning training algorithm. The bot then uses this to decide where to hit the structure with the birds.

Service discovery is an integral issue in the area of service oriented computing (SOC). A centralized platform based service discovery suffers from major drawbacks such as scalability and a single point of failure. A P2P based design incurs high maintenance overhead for a distributed service registry and querying task. In this paper, we have proposed SMARTSPACE-a hybrid multiagent based distributed platform for efficient semantic service discovery. By utilizing reactive agents in modeling services, users' requests, and registry management middleware, the proposed service discovery algorithm, SmartDiscover, is able to achieve fast, scalable, parallel, and concurrent service finding within a systemic environment that can be highly dynamic, asynchronous, and concurrent. We have conducted the SmartDiscover experiments within the JADE 3.7 agent framework on top of both IBM Cloud Cluster and NetLogo simulation environments. The results showed promising positive outcomes in terms of average query response time and the number of message exchanges to maintain the distributed registry. The accuracy of SmartDiscover was measured and compared with the widely accepted benchmark OWL-S MX approach.

Service discovery is one of the key problems that have been widely researched in the area of Service Oriented Architecture (SOA) based systems. Web Service clustering is a technique for efficiently facilitating service discovery. Most Web Service clustering approaches are based on suitable semantic similarity distance measure and a threshold. Threshold selection is essentially difficult and often leads to unsatisfactory accuracy. In this paper, we have proposed a self-organizing based clustering algorithm called Taxonomic clustering for taxonomically organizing semantic Web Service advertisements. We have tested the algorithm on both simulation based randomly generated test data and the standard OWL-S TC test data set. We have observed promising results both in terms of accuracy and performance.

The World Wide Web (WWW) has become a major platform for hosting, discovering, and composing web services. Web service clustering is a technique for efficiently facilitating web service discovery. Most web service clustering approaches are based on suitable semantic similarity distance measure and a threshold. Threshold selection is essentially difficult and often leads to unsatisfactory accuracy. In this paper we propose a taxonomic clustering algorithm for grouping functionally similar web services. We have tested the algorithm on both simulation based randomly generated test data and the standard OWL-S TC test data set. We have observed promising results both in terms of accuracy and performance.

Semantic web search involves retrieval of user-specific web artifacts by utilizing their semantic descriptions. A very specific web artifact that has evolved in recent times is web services. Web services can be semantically described in languages like OWL-S. However, such languages are limited with respect to their expressivity of context. They also lack a formal ontological framework where efficient web service retrieval can be conducted. In this paper we model a service request scenario within the web as an event-driven system. Services as well as user requests are modeled as events. We propose an ontological framework called Context-Aware Ontology Framework for Events and Services (CAOFES) where such event-driven web service retrieval can be efficiently executed by a novel reasoning technique.

The three core problems that can be identified with any SOA based system are: (i) service discovery, (ii) service selection, and (iii) service composition. Most of the previous researches have considered these three problems separately. However, we argue that these three problems are intrinsically related and hence, cannot be treated separately. We propose a proactive event-driven model where user activities and services are treated as events. We then graphically model a SOA system into a network of activities called "activity network" (ANet). The nodes of such a network are the services and the edges their mutual causal dependency. We show how both functional as well as contextual information can be incorporated into the network and then justify why that is necessary for solving the three problems mentioned hereby. We then propose an abstraction technique that helps us to simplify the ANet and provide an architectural platform where the three problems can be solved in an efficient manner.

In order to deal with dynamic events occurring in the system, we designed three basic operations over this ALNet: (i) search for end services that can trigger the target event, (ii) search for source nodes that have reachability to at least one of the end nodes, and (iii) select the source node that gives the best composition cost. However, with the addition of more and more services, the ALNet becomes larger and complex and then performing these three tasks might be computationally costly. In order to solve the problem, we proposed an abstraction method that improves the performance significantly and demonstrated that this can be an effective platform for realizing event handling in pervasive systems. As an evaluation of the ALNet, an experimental setup has been designed and the abstraction runtime performance has been tested over different sizes service network. We have found out that the abstraction runtime performance is improved (compared to one without the abstraction) and has shown a steady increase even with the increasing size of the ALNet network.

Pervasive Computing plays an integral role in our daily life. As pervasive services become increasingly important to be able to capture context, it is reasonable to speculate that dynamic representations for composition of services is very much needed. Such representations may typically include activities or functional modules that are not only temporal but also responsive to each other. Such actions may be associated with actors who execute them, spatial and temporal information, information-flow and background objects. Consequently, one may model an activity network that depicts causal relations between activities and has the capability of embedding the semantic relations of these activities with other contextual entities. In this paper we discuss a new way of representing such dynamic workflows with a novel data structure called activity logic graphs (ALGs). We introfduce a novel design of an architecture that grasps the semantics of activities (or events) in dynamic pervasive environments, maps that to a collaboration of services (as ALGs) in order to recognize specific situations (known as Situation Boundaries) that has risen out of the events, and implements the recognition process using an efficient indexing technique. We test the model with a set of ALGs and report experimental results on the queries that are essential for service composition.

Today's Web becomes a platform for services to be dynamically interconnected to produce a desired outcome. It is important to formalize the semantics of the contextual elements of web services. In this paper, we propose a novel technique called Semantic Genome Propagation Scheme (SGPS) for measuring similarity between semantic concepts. We show how SGPS is used to compute a multi-dimensional similarity between two services. We evaluate the SGPS similarity measurement in terms of the similarity performance and scalability.