Social Contact Management System for Memory Recall Enhancement

Transcription

1 Social Contact Management System for Memory Recall Enhancement Nitin Ingle 1 Prof. Ayesha Butalia 2 Pune University, GHRCEM, Pune Pune University, GHRCEM, Pune Abstract Human memory regularly comes up short. Individuals are often overpowered with inquiries like "Who is that individual? I might have met him in Mumbai? Do I?" In the recall of names successfully, current memory aid tools can't help well. This paper will investigate the memory recall enhancement issue from the viewpoint of memory signal extraction and cooperative search, and proposes a naval technique to concentrate memory signs from heterogeneous, multimodel, physical/virtual information sources. Particularly, we utilize the contact name recall as a part of the scholastic group as the target application to showcase our proposed technique. We further create a smart social contact manager that backings 1) autogathering of rich contact information from a combo of pervasive sensors and Web Intelligence sources, and 2) acquainted pursuit of contacts when human memory comes up short. By testing the execution of contact information accumulation systems, this framework is approved. A precise client consider on contact memory recall is additionally directed, through which a few discoveries about contact remembering and recall are introduced. Excellent cognitive intelligence research hypotheses are utilized to decipher these discoveries. Keywords- Contact recall, memory aid, pervasive computing, social contact management, Web intelligence. 201 I. Introduction Memory tumble is a commonly occurred problem in almost every human being. In our day-to-day life, we face diverse forms of memory lapse, such as forgetting a contact id, name of a person, name of a document etc. These memory failures do invite many types of problems in our life and work. Hence, it becomes a basic and challenging problem to find the effective and efficient way to help the human memory. Memory recall enhancement is already having been studied in different fields for different reasons. Before the computer generations, these things were manual. But, these manual methods have some drawbacks, such as failure or damage and time consuming search support. The use of digital tools has raised much attraction with the development of information technology nowadays. But somehow, these digital tools silently face some problems, while enabling reliable storage and efficient support for query. The reliability of data retrieval is the most significant issue in these overall issues. Users must remember sufficient details about items they want to retrieve. So that they can construct a query to conduct searches on data retrieval tools. But somehow, people tend to remember different memory cues related to the items, rather than remembering precise details [1], and [2]. In memory lapse problem, it has been proved to be efficient to use the memory cues. To tackle diverse memory problems, several types of cues are used commonly. But somehow, the role that a memory cue plays in memory the recall is noticeably intricate and area related. This has been of modest apprehension in existing studies. Firstly, to enhance memory recall, cues in different forms have been used, in terms of the purposes and user groups. For scholarly analysts, even then, instructive foundation and investigation

2 hobbies get to be more critical data. This is in accord with the overview result reported by Elsweiler et al. [3], which demonstrates an extensive variety of overlooking practices and hence a requirement for distinctive sorts of devices to counteract memory malfunction. It might be less demanding for the client to use a few sorts of memories over others, dependent upon the connection of memory support consequently. Next is to develop memory recall utilizing specific memory cues, we have to collect this type of cues in relationship with the things in a data document for future retrieval. One rule to be taken after here is that we ought to lighten the load of clients on memory cue accumulation [4]. Then again, auto-collection of memory cues is nontrivial. It generally relies on upon the practices of the target client bunch and the accessibility of data sources. As it were, for diverse client assembles, the data that can be gathered is distinguishing. For the individuals who are dynamic in social media sites we can separate more data like, companion records, top choices about them from the Web. In what tails, we pick the contact name memory issue, and to represent the above issue utilizing the scholastic group. The prime aim of this paper is to use memory cues for enhancement of memory recall and explored the feature-specific nature of it. We have also proposed a naval methodology to gather the memory cues from various data sources. Basically, we intend to do the 3 things, they are: Firstly, translating the memory recall enhancement problem into a well managed collection of feature-specific memory cue and the management issue as well. Secondly, we propose a simple method for enhancing memory recall using extraction of memory cues from diverse data sources. And finally, to enhance the contact management and name recall processes, we propose to design and implement the Social Contact Manager (SCM). In near future the objectives that needed to be studied are: first, as reported in other studies, the quality of the photographs or videos taken by these wearable devices might be low, and the circumstance for face recognition most of the times are difficult. Secondly, an important issue for the usage of wearable sensors is secrecy [5], [6]. The remaining paper is organized as follows. The section II includes the survey of different methodologies that were been developed using different tactics has been discussed briefly. The section III concludes the paper in brief. II. Literature Survey Use of memory cues and the improvements in social contact management systems for the enhancement of memory recall has brought closer some related search directions: A. Digital Memory Aids Digital memory aid systems basically fell in to two categories depending upon the targeted memory issues. The issues are, Retrospective aid, and prospective aid [3]. Retrospective aid basically works with the recall of the past events, previously held events; whereas, the Prospective aid deals with remembering the future events. We have concentrated our study on the retrospective aid, rather than the prospective aid. Retrospective memory aid has two important application domains: i. Digital Object Re-finding The main focus of the Digital object refinding is on the storage and search of collections of heterogeneous digital objects. These objects are either generated by the users or been encountered at the time of work processes, like s, documents etc [7] [8] [9]. Metadata on the diverse aspects of an object s context like, when, where and relationships amongst them, is used commonly by tools. This makes the re-finding of the information much easier. ii. Life-Logging 202

3 Unlike the digital object re-finding, the Life-Logging extends beyond the storage of objects in closed environment, rather it moves in the real world. Because of this, it can capture our common activities. For Lifelogging, various systems have been developed. These systems commonly use the portable and sometimes wearable devices for capturing the data. A personal digital assistant [PDA] is used by the Forget-me-not [10]. Because of this, it can capture the activities of its users in the text form, i.e. user locations etc, daily. B. Human-Centric Computing The context-aware services are provided by the Human-centric computing with respect to the extracted information from the cyber-physical space about the user. There are two major sources for extracting the human information: web and pervasive sensors. Both these sources acquire the different attributes and strengths. The web is the best source for the static or slowly changing human information. This information may include the user profiles [11], social relationships etc. whereas; the pervasive sensing allows detecting the human activities, their social interactions [12], etc within the existent world. Combined data from these distinct sources allows exclusive opportunities to pervasive applications because of the diverse features [13]. But somehow, till the date, both these sources have followed the different research fields. Few systems have been developed in order to combine the power of these two data sources additionally. We have proposed a unique method to auto collect the digital traces of humans from the heterogeneous datasets. C. Social Contact Tools For getting the work done, personal contacts are critical. In social activities, for assisting the communication with contacts and the recommended contacts, many systems have been developed. i. Contact communication Environments suiting electronic communications like, s, instant messaging or social networking sites have become increasingly reliable for the modern work. An alternative representation of the s is provided by the ContactMap [14]. A problem that occurs regularly with such systems is that, they are strictly created for the communications between registered users in the virtual world. A general way of allowing people to create digital connections with their contacts is proposed by the Social Contact Manager (SCM). Exchanging the business cards in real world is the only source for connections ii. Contact recommendation Many systems have been developed to recommend the users with the new contacts. The recommendations are based on the background, social relationships, or context of the users. The users inside a public place, like hotels, can exchange the social network IDs by using their mobile phones and find someone of their interest. The WhozThat [15] provided this unique feature. Our system tends to support the management and already acquired information s recollection, rather than giving a social matching service. III. Existing Systems Bin Guo et al [1] proposed a system that enhanced the memory recall process using the memory cues. This system is shown in fig (1). In this system, the audio clips are used as an input. These audio clips 203

4 are recorded during the meetings. The important phases of this system are: Fig (1) System architecture of Bin Gou 1. Pre-processing the audio clip: The pre-processing necessary for extracting the actual audio from the clip is the main feature of this phase. It includes, Framing, pre-emphasis, and FFT phase. 2. Feature Extraction: In this phase, the important features from the clip are extracted. These features will work as the memory cues. This includes few techniques for extracting the features, like Haar, SF, and ZCR etc. 3. Finally, The Decision Tree classier is used to generate the required output from the audio clips. 4. The main drawback of this system is it only works with the audio clips. The remaining information was needed to be filled manually by the user. Thus, a new system was needed. T. Kawamura et al [2] has proposed a system that is based on the object-triggered augmentation of the human memory system, called as Ubiquitous Memory. This system enables the user to directly store the experienced events by Touching operation in a physical object. Fig (2) sows the architecture of the system. Fig (2) Kawamura s Architecture The drawback of this system was the security. Y Chon et al [6] have proposed a system called CrowdSence@Place (CSP). This framework was used for categorization of the places, based on the sensor data. This data consist of opportunistically captured images and the audios from the users of Smartphone. The main objective of this paper was to collect the places and categorize them correctly. Fig (3) shows the architecture of the system. The main drawback faced by this system was the accuracy of the classification, which was not up to the needed level. 204

5 the purpose of data search, it uses traditional information search technique. Additionally, this system also gives the face detection feature. Fig (3) architecture IV. Proposed Work The major intend of this paper is to employ memory cues for enrichment of memory recall and explored the featureprecise characteristics of it. We have proposed a new technology to collect the memory cues in the form of Videos, Images, Audios and Texts from different sources of data. The meetings are recorded in some device which is connected to the mobile by some wireless technology. Then these videos are forwarded to the server. The location is also detected and sent using the GPS. The processing of these videos includes following steps: 1. These video clips are processed in order to extract the audio, images, from the file. These extracted audio clips and images, along with the original video are sent to server. 2. These data is then pre-processed using some tokenizing technique. 3. The framing is done on all these data, in order to extract the features. 4. These features are then stored in database, while, the useless data is neglected. This system also allows the user to share this information on the social networking sites. For Fig (2) Flow of Proposed system V. Conclusion The current paper reports our deliberations on memory recall utilizing memory cues. The approach we propose addresses the impacts of distinctive peculiarities to cue-based memory recall. What's more, the technique is proposed, which investigates a combination of both pervasive sensing and Web brainpower strategies to auto-accumulate memory cues from physical/virtual information resources. The process is exhibited through a research endeavor the Social Contact Management (SCM). The SCM was intended to address the successive contact name slipping issue. The MRSS methodology was investigated to record rich contact information from both true collaborations and the Web. Our framework can adequately concentrate contact cues from 205

6 heterogeneous sources as demonstrated by the assessment results. The increased contact recall strategy was approved through a client trial. Our study on increased memory recall is continuous. To begin with, as the ID of semantic relations among individuals is essential to utilize our SCN better, past the advisor student connection abused in this paper, we want to discover more social relationship data later on to broaden the SCM. Secondly, we will research the exploitation of diverse sorts of connections, to upgrade contact memory recall 206 References [1]. Bin Guo, Daqing Zhang, Dingqi Yang, Zhiwen Yu, and Xingshe Zhou, Enhancing Memory Recall via an Intelligent Social Contact Manage-ment System, IEEE Transactions On Human- Machine Systems, [2]. T. Kawamura, T. Fukuhara, H. Takeda, Y. Kono, andm. Kidode, Ubiquitous memories: A memory externalization system using physical objects, Pers. Ubiquitous Comput., [3]. D. Elsweiler, I. Ruthven, and C. Jones, Towards memory supporting personal information management tools, J. Amer. Soc. Inform. Sci. Technol., [4]. S. Whittaker, Q. Jones, B. Nardi, M. Creech, L. Teryeen, E. Isaacs, and J. Hainsworth, ContactMap: Organi-zing communication in a social desktop, ACM Trans. Computer-Human Interaction, [5]. S. Hodges, L. Williams, E. Berry, S. Izadi, J. Srinivasan, A. Butler, G. Smyth, N. Kapur, K. Wood, SenseCam: A retrospective memory aid, in Proc. UbiComp, [6]. Y. Chon, N. D. Lane, H. Cha, F. Zhao, Automatically characterizing places with oppotunistic crowdsensing using smartphones, in Proc. ACM UbiComp, [7]. D. Elsweiler, M. Baillie, and I. Ruthven, Exploring memory in refinding, ACM Trans. Inf. Syst., [8]. D. H. Chau, B. Myers, and A. Faulring, What to do when search fails: Finding information by association, in Proc. Conf. Human Factors Comput. Syst., [9]. D. Zhang, B. Guo, and Z. Yu, The emergence of social and community intelligence, IEEE Comput., [10]. M. Lamming and M. Flynn, Forgetme-not: Intimate computing in support of human memory, in Proc. FRIEND21, [11]. J. Tang, D. Zhang, and L. M. Yao, Social network extraction of academic researchers, in Proc. IEEE Int. Conf. Data Mining, [12]. D. Quercia, J. Ellis, and L. Capri, Nurturing social networks using mobile phones, IEEE Pervas. Comput., [13]. D. Zhang, B. Guo, and Z. Yu, The emergence of social and community intelligence, IEEE Comput., [14]. S. Whittaker, Q. Jones, B. Nardi, M. Creech, L. Teryeen, E. Isaacs, and J. Hainsworth, ContactMap: Organi-zing communication in a social desktop, ACM Trans. Computer-Human Interaction, [15]. A. Beach et al., WhozThat? Evolving an ecosystem for context-aware mobile social networks, IEEE Netw., 2008.