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IoT (Internet of Things) frameworks are designed to simplify the development process of IoT devices and applications. However, like any technology, they also have some problems that need to be addressed. Some of the common problems associated with IoT frameworks include:• Security Concerns: IoT devices collect and transmit sensitive data, making them a prime target for cyberattacks. Many IoT frameworks lack built-in security features, making it easier for hackers to access the data.• Interoperability Issues: IoT devices from different manufacturers may use different protocols and standards, making it difficult for them to communicate with each other.• Scalability Challenges: IoT systems can involve large numbers of devices and applications, and the frameworks used to develop these systems need to be scalable. This requires significant computing power and data storage capacity, which can be a challenge for some IoT frameworks.• Complexity: IoT development can be complex, as it involves multiple components and technologies, such as cloud computing, big data analytics, and edge computing. The frameworks used to develop IoT systems need to be able to integrate these components and simplify the development process.• Cost: Developing and deploying IoT systems can be expensive, as it requires specialized hardware, software, and infrastructure. The cost of IoT frameworks can also be a barrier to adoption for some organizations.Just like commercial IoT frameworks, Department of Defense (DoD) unattended ground sensors (UGSs) come in many shapes and sizes and often have many different components. These components range from infrared cameras and magnetometers to communications equipment and embedded computers. The combination of these different components requires sophisticated software that is often time consuming to develop and difficult to reuse and have the same problems like commercial IoT systems.The US Army Research Laboratory (ARL) and the Defense Intelligence Agency funded the Open Standards for Unattended Sensors (OSUS) to address solving these problems.3 The OSUS program, formerly named Terra Harvest, was launched in 2009 to develop an open, integrated battlefield unattended ground sensors architecture that ensures interoperability among disparate UGS components and systems.The University of Dayton Research Institute (UDRI) software system group (SSG) was chosen to work with the ARL to create a reference implementation of the OSUS standard. ARL along with UDRI went to various field-testing and demonstrations to test out OSUS in real-life simulations, integrating various vendors sensor, controllers, radios, using the OSUS simple plug-in-play architecture showing a solution that overcame the typical problems of UGS/IoT systems.After years of field success, the US Army reclassified the technology, allowing it to be used by the public. This has led it to become the standard for various US agencies and The Five Eyes Alliance (FVEYs) intelligence communities. At the FVEY The Technical Cooperation Program’s (TTCP) Contested Urban Environment Strategic Challenge (CUE17, 18, 19, and 20 for short), OSUS was the only middleware layer for all of the events. The author led the team to integrate all the various worldwide sensors/systems using OSUS. OSUS has shown through all these demonstrations that it is able to integrate world-wide sensors/systems into a common framework, eliminating most of the problems with UGS/IoT systems as stated above.In this thesis, I will attempt to show that OSUS offers a solid framework to use for IoT. The paper will provide a background of what IoT is, along with the architecture of OSUS. It will then show how one can create a plugin for OSUS and lastly, how to deploy/test these plugins.

A significant aspect of communication between academic research and technology development is through various forms of publications. Many educational professionals, scholars, and students spend significant amounts of their time writing research papers, journal articles, conference papers, and other types of scholarly text. Microsoft Word is a well accepted document preparation system. To improve the usability of document authoring, this research proposes an add-on for Microsoft Word, which recommends the keywords as well as related hyperlinks.The keywords are extracted using the concept of Automatic Keyword Extraction. A domain-independent algorithm, Rapid Automatic Keyword Extraction (RAKE) is used to retrieve the keywords from the document. A custom Google search option is included in this add-on to find out the related hyperlinks for the keywords.

Google and other search engines feature the entity search by representing a knowledge card summarizing related facts about the user-supplied entity. However, the knowledge card is limited to certain entities which have a Wiki page or an entry in encyclopedias such as Freebase. The current encyclopedias are limited to highly popular entities which are far fewer compared with the emerging entities. Despite the availability of knowledge about the emerging entities on the search results, yet there are no approaches to capture, abstract, summarize, fuse, and validate fragmented pieces of knowledge about them. Thus, in this paper, we develop approaches to capture two types of knowledge about the emerging entities from a corpus extended from top-n search snippets of a given emerging entity. The first kind of knowledge identifies the role(s) of the emerging entity as, e.g., who is s/he? The second kind captures the entities closely associated with the emerging entity. As the testbed, we considered a collection of 20 emerging entities and 20 popular entities as the ground truth. Our approach is an unsupervised approach based on text analysis and entity embeddings. Our experimental studies show promising results as the accuracy of more than 87% for recognizing entities and 75% for ranking them. Beside 87% of the entailed types were recognizable. Our testbed and source codes are available on Github (https://github.com/sunnyUD/research_source_code).

This research is about information technology (IT) and merger and acquisition(M&A). In recent years, M&A has been a common tool and a fast way for companies to grow and do business. Thus, M&A activity still continues to flourish despite the current economic downturn. M&A is a business transaction and IT has become an increasingly important part of the business. Studies show that while many M&A have failed for different reasons, the major reason is that the Chief Executive Officer (CEO), the Chief Information Officer (CIO), and the key handler of the M&A project did not bring the IT department on board early in the project, due to lack of understanding of the critical role that IT plays in M&A deals. In order to successfully execute M&A, it is essential to have a well-built IT department as a model for M&A with well thought out governance and policies in order to set a good foundation for a successful M&A project. This paper offers some viewpoints, approaches, and methods to help ensure the success of the M&A project; this paper also illustrates some real world successful examples which show the M&A project's ability to perform the synergy capture which drives most M&A.

In this paper, we explore the concept of epistemic visualization in interrogative domains. Epistemic visualization is the process and result of developing visual models that capture the structure, content, justification and acquisition of knowledge obtained by a software agent in a knowledge-based system. The knowledge is the foundation in which the agent can respond to queries against a corpus containing questions and answers. The visualizations are therefore used to examine the quality of the software agent's knowledge. The visual models will include justification and commitment artifacts as well as knowledge acquisition flow. The visualization will demarcate the a priori and posteriori knowledge. The knowledge of the software agent is stored in epistemic structures which are knowledge representation schemes that supports the basic concepts of knowledge as defined by the tripartite analysis of knowledge. Epistemic visualization is used to analyze the quality of the knowledge of a software agent in an interrogative domain. For our purpose, interrogative domains are hearings, trials, interrogations, personality test or any document source in which the primary content is questions and answers pairs. In this paper, we introduce the Epistemic Structure Es that captures the agent's knowledge and the visualization of that epistemic structure using common visualization techniques.

At Ctest Laboratories we are exploring the notion of automated conversion of the semi-structured text to an epistemic structure suitable for deductive inference. In this paper we will develop an epistemic structured representation for electronic transcripts ofinterrogative domains. We propose that knowledge which is typically not visible to keyword search or string matching, can be readily extracted from the an electronic transcript when it is given an appropriate epistemic structure. We introduce an Epistemic Structure Es and a process for converting a semi-structured transcript from and interrogative domain to Es. In this paper we restrict our discussion and analysis to transcripts that have been stored as semi-structured text. In particular we are interested in any knowledge that can be deduced by an interrogative agent from the content of an electronic transcript. Further we develop the notion of an interrogative agent that relies on epistemic justification as a condition for knowledge.

In an earlier paper, a method called One-class Classification using Length statistics of (jumping) Emerging Patterns (OCLEP) was introduced for masquerader detection.Jumping emerging patterns (JEPs) for a test instance are minimal patterns that match the test instance but they do not match any normal instances.OCLEP was based on the observation that one needs long JEPs to differentiate an instance of one class from instances of the same class, but needs short JEPs to differentiate an instance of one class from instances of a different class. In this thesis, we present OCLEP+, One-class Classification using Length statistics of Emerging Patterns Plus by adding several new ideas to OCELP. OCLEP+ retains the one-class training feature of OCELP, hence it only requires the normal class data for training. Moreover, OCELP+ has the advantage of being not model or signature based, making it hard to evade. OCLEP+ uses only length statistics of JEPs, making it a robust method. Experiments show that OCELP+ is more accurate than OCLEP and one-class SVM, on the NSL-KDD datasets.

With the growth of the Internet to an omnipresent system, access has become easier than ever, and its use has grown drastically. With this growth, however, comes drawbacks. In this growth, criminals are trying to gain unauthorized access to systems and the information contained therein. While for an average citizen, this breach might have only a few smaller impacts, this could be devastating if a critical target is affected. To prevent this, security technologies have been developed to try and prevent these breaches from occurring. However, this security can end up causing problems for legitimate investigations as much as it does for illegal access. This paper looks at how the growth of information and communication technologies in the modern era and the security systems protecting them affect how the state operates.

We propose a new visualization interface for navigating the ACM digital library. It supports effective literature exploration with a set of web-based functions including search, detail summary, conference summary, author summary, and citeology. These functions are designed and integrated with enhanced perceptual understanding and human-machine interaction, where colors, diagrams, layouts and other informative visualization factors are utilized to analyze the collective metadata from the ACM digital library. We use a large-scale data set of the titles, authors, categories and abstracts based on the ACM digital library. A phrase extraction algorithm is designed to retrieve meaningful phrases from the data set. All the web based functions mentioned above uses this algorithm. These phrases, instead of single keywords, can represent the publications with improved semantics, which enhance the visualization output and user experience. We do this by sequentially scanning the paper’s abstract and using pre-defined dictionaries, punctuations to pull out groups of meaningful phrases and throwing out junk words such as verbs, possessive pronouns and other pre-defined stop words that would prevent us from returning meaningful data. The visual interface provides an advanced platform for researchers in their literature study. It can be further extended to the exploration of other libraries and databases.

One of the most popular Internet applications is peer-to-peer (P2P) networking. P2P networking is an application in which multiple computers communicate over a network to share resources, such as files. The effectiveness of P2P networks is enhanced when multiple computers host a similar resource; this is called redundancy. Because its architecture requires computers to remain powered on for file sharing and redundancy, much power is consumed to operate these networks. One way in which researchers are working to solve this problem is by developing heuristics for reducing the amount power consumed by computers in P2P networks. While heuristics are best tested on real networks, doing so can be both challenging and expensive. This paper describes a solution in FrAPPE, a program in which heuristics may be tested in a simulated networking environment. In addition, FrAPPE compiles statistical information regarding the power consumption and computational expense of heuristic trials.As a simulated networking environment, FrAPPE also doubles as a teaching tool for computer networking education. A number of different software tools exist that are often used to teach computer networking concepts in academic environments. No programs known to the author, however, illustrate the costs incurred by computer networking hardware. FrAPPE offers a simple environment that demonstrates basic networking concepts, and, through its statistical output, helps students understand the aforementioned costs.

Purpose: As healthcare organizations expand the scope of their operations with an eye towards cost reductions, quality improvements, sustainability, increased stakeholder satisfaction and increased performance, they are increasingly investing significant resources into information systems in general and Business Intelligence Systems (BIS) in particular to provide the necessary operational and decision support information. This paper seeks to model the relationships between BIS, learning, quality organization and competitive performance, as well as measure the influence BIS has on end-user perceptions of quality and competitive performance from a learning point of view. Methods: Qualitative and quantitative methods including survey, interview, and case study instruments to measure the link between BIS, learning models of mental-model building and mental-model maintenance, quality organization, and competitive performance. Individual, organizational, system, information, and service characteristics are explored to measure the relationship between variables. Extending models from prior-literature, a proposed model is introduced to improve the explanatory power of the prior model, and extend theoretical, practical, and policy contributions within a healthcare setting. Results: Results demonstrate a significant relationship between learning, quality and competitive performance when utilizing BIS. Information and system quality characteristics also influence the level of learning. The model increases the explanatory power over the prior information support systems and learning models and adds important contributions to healthcare research and practice. Contribution: Technology improvements and cost reductions have allowed BIS to be extended to the entire set of organizational stakeholders to provide information for various forms of decision making. Despite these improvements, there is still a significant organizational investment and risk to implement and maintain BIS. Expectations and funding for BIS in healthcare are based on the desire for improved quality and competitive performance across all levels of the organization. Previous information systems have not demonstrated a link between multiple forms of learning and competitive performance. In addition research does not directly address how BIS and learning impact healthcare quality or discuss BIS specific relationships specifically within a healthcare setting. BIS which expand the inherent information analysis capabilities and expands usage to all stakeholders demonstrates a link between both mental-model building, mental-model maintenance as a component of learning, quality organization and competitive performance. Through democratizing BIS, organizations can become sustainable through improved healthcare quality and competitive performance across all employee levels.