On Repairing Reasoning Reversals via Representational Refinements

Representation is a fluent. A mismatch between the real world and an agent’s representation of it can be signalled by unexpected failures (or successes) of the agent’s reasoning. The ‘real world ’ may include the ontologies of other agents. Such mismatches can be repaired by refining or abstracting an agent’s ontology. These refinements or abstractions may not be limited to changes of belief, but may also change the signature of the agent’s ontology. We describe the implementation and successful evaluation of these ideas in the ORS system. ORS diagnoses failures in plan execution and then repairs the faulty ontologies. Our automated approach to dynamic ontology repair has been designed specifically to address real issues in multi-agent systems, for instance, as envisaged in the Semantic Web

Spartan Daily, February 17, 1964

Volume 51, Issue 71https://scholarworks.sjsu.edu/spartandaily/4552/thumbnail.jp

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field

Supporting Telecommunication Alarm Management System with Trouble Ticket Prediction

Fault alarm data emanated from heterogeneous telecommunication network services and infrastructures are exploding with network expansions. Managing and tracking the alarms with Trouble Tickets using manual or expert rule- based methods has become challenging due to increase in the complexity of Alarm Management Systems and demand for deployment of highly trained experts. As the size and complexity of networks hike immensely, identifying semantically identical alarms, generated from heterogeneous network elements from diverse vendors, with data-driven methodologies has become imperative to enhance efficiency. In this paper, a data-driven Trouble Ticket prediction models are proposed to leverage Alarm Management Systems. To improve performance, feature extraction, using a sliding time-window and feature engineering, from related history alarm streams is also introduced. The models were trained and validated with a data-set provided by the largest telecommunication provider in Italy. The experimental results showed the promising efficacy of the proposed approach in suppressing false positive alarms with Trouble Ticket prediction

Machine Learning for the New York City Power Grid

Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce (1) feeder failure rankings, (2) cable, joint, terminator, and transformer rankings, (3) feeder Mean Time Between Failure (MTBF) estimates, and (4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or real-time, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The “rawness” of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City's electrical grid

Character Strengths as a Pathway to Obtaining and Maintaining Employment for Job Seekers with Disabilities: A Model for Building Job Seeker Hope and Self-Efficacy

Americans with disabilities experience low rates of employment and are more than twice as likely to live in poverty than their non-disabled peers. Job seekers with disabilities face a myriad of external barriers to employment: 1) lower education/skill attainment, 2) complex public benefit rules, 3) limited access to reliable transportation, 4) lack of work-related supports, and 5) persistent employer-bias. These challenges are compounded by internal barriers: 1) diminished self-efficacy and hope, 2) social role devaluation, and 3) extrinsic work motivations. Several theories within positive psychology offer new pathways to employment for people with disabilities. One such theory is character strengths. Character strengths offer a framework for understanding who we are at our core and how to leverage our strengths to improve outcomes in multiple areas of life. Research on character has led to the design of interventions to build hope and self-efficacy, and to foster goal achievement. This paper will review research on character strengths as a facilitator of employment for job seekers with disabilities and provide an outline for integrating character-based positive interventions into a national employment program for people with acquired physical disabilities

Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios

odern service providers often have to deal with large amounts of customer requests, which they need to act upon in a swift and effective manner to ensure adequate support is provided. In this context, machine learning algorithms are fundamental in streamlining support ticket processing workflows. However, a large part of current approaches is still based on traditional Natural Language Processing approaches without fully exploiting the latest advancements in this field. In this work, we aim to provide an overview of support Ticket Automation, what recent proposals are being made in this field, and how well some of these methods can generalize to new scenarios and datasets. We list the most recent proposals for these tasks and examine in detail the ones related to Ticket Classification, the most prevalent of them. We analyze commonly utilized datasets and experiment on two of them, both characterized by a two-level hierarchy of labels, which are descriptive of the ticket’s topic at different levels of granularity. The first is a collection of 20,000 customer complaints, and the second comprises 35,000 issues crawled from a bug reporting website. Using this data, we focus on topically classifying tickets using a pre-trained BERT language model. The experimental section of this work has two objectives. First, we demonstrate the impact of different document representation strategies on classification performance. Secondly, we showcase an effective way to boost classification by injecting information from the hierarchical structure of the labels into the classifier. Our findings show that the choice of the embedding strategy for ticket embeddings considerably impacts classification metrics on our datasets: the best method improves by more than 28% in F1- score over the standard strategy. We also showcase the effectiveness of hierarchical information injection, which further improves the results. In the bugs dataset, one of our multi-level models (ML-BERT) outperforms the best baseline by up to 5.7% in F1-score and 5.4% in accuracy

Use of electric money in Japan

This paper will take a look at electric money and demonstrate that it cannot entirely replace cash in Japan. With the progress of Information Technology (IT), Electronic Commerce (EC, e-commerce) has recently expanded into the society in Japan. However, since Japan is still a developing nation in IT, the government has decided to advance the plan eJapan in 2001 that aims to make Japan the leading IT nation in the world within five years with all of the citizens actively using IT (IT Square, 2003). In the plan, the government has largely focused on five projects, which are the construction of the high-speed network infrastructure, the spread of IT training, the realization of an electronic government, the realization of keeping high security and credibility in the Information network, and the development of e-commerce (Fujisawa, 2001). The government has planned to activate the country\u27s economy with the spread of e-commerce. In this context, electric money is in the spotlight as a payment tool of the next generation. Although some ways of settlement such as credit cards are now used, it is projected that electric money will become more popular in the future because of its advantages such as high security or instant settlement in comparison to other ways of payment. Besides, it is also predicted that electric money could replace cash (NTT, 2000). However, the currency of electric money is still in the tentative stage, and the adoption of electric money among the general public is still low. In addition, there are various problems or barriers preventing the prevalence of electric money. For example, all the past tests of popularizing electric money in Japan ended in failure due to the inconvenience of using it. There is a strong custom that the Japanese people mainly use cash for the shopping, whereas checks or credit cards are universal in the Western countries. There are other challenges. People are worried about crimes such as forgery and robbery of electric money in terms of security. The definition of electric money in the law is also very complicated. All of these affect the spread of electric money. There are different opinions about electric money in Japan. One is that cash could be superseded by electric money in the future. The other is that the former opinion is rather wishful thinking, and electric money will only partly prevail as one of the payment ways. At any rate, it is said that people will readily use electric money if it is really convenient and safe to use. Both the public and the government have taken note of the future of electric money. In this connection, it is worthwhile to examine electric money and show its possibilities. First, this paper will briefly explain the basics of electric money such as its origins, varieties and characteristics of electric money. Second, this paper will examine some examples of the past experiments and the current conditions of electric money in terms of usability. Third, this paper will examine the security of electric money. Fourth, this paper will inspect the law for supporting the use of electric money. Fifth, this paper will discuss the culture that affects on the prevalence of electric money. Finally, this paper will draw a conclusion that electric money could not entirely replace cash in Japan, namely it could only be an alternative payment way with collecting these bases