Meet four is a crowd

Four's a Crowd () - IMDb

meet four is a crowd

Directed by Djonny Chen. With Alison Dobbin, Catherine Adams, Kim Seybold, Alan Raine. Jessica, Wendy and Suzanne meet up every Friday evening at a bar . The Place Where We Will Meet / Four Is A Crowd (hidden track) by Syrinx Call, released 27 November Directed by Kinga Luczkiewicz, Kuba Luczkiewicz. With Shannon Brown, Suzette Brown, Ashley Lobo, Paul Pikus. When two couples meet for a casual dinner.

meet four is a crowd

In particular, metadata gathered from users of social media can be used for a variety of marketing, medical, and other applications by these industries. Crowd knowledge refers to processes, activities, and resources that are created and deployed by large, often organization-independent user bases [ 13 ].

Crowd knowledge is self-evolving, rapidly changing, domain-based knowledge acquired using social media, search engines, or other open source origin [ 13 ]. The beauty of crowd knowledge is that it is open sourced and freely accessible, and it can therefore be mined very easily through the Internet using social media, or search tools.

Its coverage is broad, as data from all over the world may be collected from anyone who wants to participate.

Meet the Crowd

Crowd knowledge can be less biased than knowledge collected from a relatively small set of data sources, such as in studies using case-based reasoning. Crowd knowledge can be unreliable because participants who provide data have no obligation or responsibility to affirm that the knowledge is correct. Since the intent of users of crowd knowledge cannot be identified in advance, the knowledge is not well structured according to specific uses.

These constraints decrease the accuracy of classifications using crowd knowledge. In this study, we propose a hybrid reasoning approach combining case-based reasoning as a form of collective knowledge and big data search engine as a source of crowd knowledge. Search engines such as Google are open source tools for continuously accumulating data storage tools.

It has less biased collective data, good coverage over time, and unbiased data collection methods, which make it less susceptible to the cold-start problem. We used an actual dataset from a commercial wellness care service, operating in China and Korea, to demonstrate the performance of this hybrid method. The paper is organized as follows: Section 2 provides a literature review on case-based reasoning and crowdsourced knowledge.

The proposed method is outlined in Section 3. The experiment and results are described in Sections 4 and 5respectively. Section 6 concludes and gives areas of future research. Case-Based Reasoning During problem-solving, human beings naturally reuse previous knowledge, accessing information about similar past cases for which they have information.

In machine learning, systems and methodologies have been developed to solve new problems in ways similar to human problem-solving. Case-based reasoning CBR is one such methodology. CBR is the process by which new problems are solved based on past experiences, where problems were solved and the routines for doing so were memorized [ 18 ]. CBR is a methodology combining problem-solving and learning. In the s, use of CBR grew widely in various fields.

Bhushan and Hopkinson [ 25 ] applied CBR to global searches for reservoir analogues. Combined with database systems, CBR has been used to support the interpretation and classification of new rock samples [ 26 ].

Particularly relevant to this study is the fact that many CBR systems have been applied in medical decision making, such as CBR for medical knowledge-based systems [ 28 ], CBR in the health sciences [ 29 ], CBR for the prognosis and diagnosis of chronic diseases [ 30 ], case-based medical diagnosis, development, and experimentation [ 31 ], and a distributed CBR tool for medical prognosis [ 32 ]. Recently, CBR has been applied in problem-solving complex domains, including planning [ 33 ], law [ 34 ], e-learning [ 35 ], knowledge management [ 36 ], image processing [ 37 ], and recommender systems [ 38 ].

meet four is a crowd

CBR follows a cycle of four steps or processes: A typical CBR system might look something like this: Based on these principles and previous studies of CBR, the benefits of using CBR in solving real-world problems are as follows: Despite the widespread use and acceptance of the CBR method, without statistically relevant data for backing up the results and facilitating generalization, there is no guarantee that the results of problem-solving using CBR are correct [ 39 ].

This well-known problem is called the cold-start problem in information systems, based on computer automation of the data modelling. Obtaining sufficient and appropriate information is an intrinsic problem so that statistically relevant inferences can be made. Although the relevant data may generate a result that solves the problem, without sufficient data to back up the results, no statistically sound claims can be made.

In addition, class imbalance, by having the total number of data in some classes more numerous than others, exacerbates the cold-start problem. In this study, the authors examine the potential of crowd knowledge-based methodology to overcome this limitation of CBR. Class Imbalance Class imbalance causes and exacerbates the cold-start problem [ 24 ]. Class imbalance refers to the problem that some classes have many more instances than others.

The class imbalance problem is encountered in a large number of domains. For example, in medical diagnosis, class imbalance is often found. The stakes are high in this field because it is necessary for classifiers to be accurate. The cost of erroneously diagnosing a patient as healthy may exceed that of mistakenly diagnosing a healthy person as sick, because the former error may result in loss of life [ 7 ]. Class imbalance is common when the collected knowledge is insufficient because it is new or there is a change in the environment, like a fluctuation in consumer preference.

Class imbalance contributes to the cold-start problem, which occurs in situations where decisions or historical data are required but for which no dataset has yet been established. This is a widespread problem in recommender and diagnosis systems.

The implicit and explicit preferences may be related to other users, item attributes, or contexts. For example, a travel recommender provides decision options for specific users from combined preferences, like explicit rating information e. Researchers have developed algorithms and techniques to avoid the cold-start problem caused by class imbalance, including memory-based algorithms [ 40 ], filtering through hard-clustering [ 41 ], simultaneous hard-clustering [ 42 ], soft-clustering [ 43 ], singular value decomposition [ 44 ], inferring item-item similarities [ 45 ], probabilistic modelling [ 46 ], machine learning [ 47 ], and list ranking [ 48 ].

These techniques all have both advantages and disadvantages. Other methods to cope with the class imbalance problem with the cold-start problem must be developed. On a Web 2. Examples of Web 2.

The Place Where We Will Meet / Four Is A Crowd (hidden track) | Syrinx Call

Furthermore, the potential for knowledge sharing today is unmatched in history. Never before have so many creative and knowledgeable people been connected by such an efficient and universal network. The Internet provides an incredible wealth of information and diversity of perspective and fosters a culture of mass participation that sustains a fountain of publicly available content.

Millions of humans upload their knowledge online for easy storing, searching, and sharing with others. Crowd knowledge is an imprecisely defined term.

In this paper, crowd knowledge is treated as an extension of collective intelligence. Ever wonder how Google Maps tends to be so spot on? More significant contributions come with greater rewards, too: Knowledge is for everyone, and the democratization of knowledge is the fundamental principle that led Jimmy Wales to start Wikipedia.

The internet encyclopedia project is free of charge and allows users to copy and change it as long as they follow certain guidelines. The project has been alive sinceand is available today in active language editions with 41, total registered users. The user community actively edits and adds information, and the encyclopedia is always in a constant state of update and improvement. Through this move, Airtel wants to create a seamless mobile experience for customers by partnering with them to improve its network.

The Open Network website allows users to track coverage and signal strength across the country in real-time and provides a direct channel for communicating leaks in service. It even lets customers offer up space for placing new towers in areas they are planned or were forcibly shut down. This will help Airtel learn which spots need better connectivity and enable it to improve the network in those areas.