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This study aims to trace or establish a neutral point that excess amount of digitalization has tampered the standard language or human beings are once again heading toward an evolution of new form of youth language which will carry within it the germs of Acronyms, breeding into a new form of language. Though CMC computer mediated communication, and teaching through it has been there in English Language Teaching since long time. But recently the development in the fields of technology, has begun to take its toll on the language (master) of youngsters altering their word usage in spoken & written form. The internet and its shortcut language have an adverse effect on young learners as per many linguists. But on the contrary a fact cannot be denied that the usage of textese or acronyms are absolutely personal and context based. My research will try to delve into, whether this increasing trend of using Textese in WhatsApp is altering or having an adverse effect on language? Turning it into an abbreviated form or rather it’s just another stereotypical notion against a new invention. Also, my study will include that WhatsApp usage and its influence on the younger generations as to the excess usage of Textese hampering their ability to learn the textual language or the inability to communicate in a standard language as said by, Humphry’s and Sutherland. Finally, I will conclude that linguistic feature of WhatsApp is different from that of other social media or how ICT has now become an important part of communication, making the social media active in language development, also some of the faltering elements that WhatsApp has inflicted on the textual or standard language.

Vehicles, both personal and commercial, have become ubiquitous forms of transportation in the developed world. The auto industry is amidst a technological transformation in identifying alternative sources of energy to power vehicles due to two driving forces: environmental pollution prevention and depletion of fuel resources. This driver for developing “smarter” solutions to create a “smarter planet” is crucial to advancing the science behind electric vehicles (EVs). EVs have been in existence since the mid-19th century, and electric locomotion has been the commonplace in many other vehicle types such as trains. The focus of this chapter is to discuss the feasibility of EVs in smart cities. In particular, the chapter explores the types of EVs, advantages and challenges faced by EVs to penetrate the market, and to outline state-of-the-art research and technologies that are driving the creation of newer and better EVs for adoption in the smart cities of tomorrow.

Electric vehicles (EVs) are gaining popularity amongst everyday commuters, but are still having a hard time penetrating the fleet vehicle market. In this paper, we investigate a model which does not require changes to current street infrastructure using charge sharing through inductive power transfer to determine if we can extend the driving range of EVs enough such that taxicab fleets can become EVs. Using fisheye state routing to coordinate rendezvous points, and a simple heuristic where cars exchange charge when there is excess available, we show that we can effectively increase the EV driving distance without needing to recharge. We use real data from the New York City Taxi and Limousine Commission to model taxicab usage behavior and the United States Department of Transportation Omnibus Survey to model commuter vehicle usage. Using this data, we demonstrate that an electric taxicab fleet could function with minimal disturbance (failure < 5%).

Battery electric vehicles (EV) average 75 miles on a fully charged battery. This range can be limited by external factors, namely use of battery power while the vehicle is stopped in traffic. Coupled with the time required by a battery to fully recharge and the current lack of charging stations, many drivers have range anxiety which prevent them from switching to EVs. One method to increase interest in EVs and to ease range anxiety concerns among drivers is to offer immediate charging facilities that are available to drivers. A possible solution is to have drivers "share" charge with each other using inductive power transfer to wirelessly transfer charge between vehicles at rendezvous points. We simulate the interactions of this ad-hoc network of rendezvous points between EVs for charge transfer to determine the feasibility of such a system, especially for taxicabs. We use real world data to simulate typical commuting driving distributions for commuters and New York City taxis. Assuming that all users in the system use GPS, we know each cars start and stop nodes. Using fisheye state routing (FSR) with first-come-first-serve (FCFS) methodology for car coordination at rendezvous points, we show that we are able to reduce the probability of refueling to 0% for commuter cars and significantly lowers the probability of refueling for taxicabs traveling more than 75 miles.

Electric vehicles have a limited driving range due to current battery capacities. One alternative to larger batteries is sharing charge between vehicles on the road and thereby forming a moving network of vehicles. This is made feasible by the recent advances in inductive power transfer technologies. This paper will demonstrate the feasibility of using this technique through a simulation. We show that with the currently available state of the art technology, our charge sharing method can increase the distance a vehicle travels by more than 50% for a standard driving distribution beyond the standard 100 miles that a vehicle usually travels on one fully charged battery. By adding pricing incentives through game theoretical approaches as well as communication strategies for cars to set rendezvous points, we can ensure participation in our system and symbiotic success for vehicles involved.

The human voice is primarily a carrier of speech, but it also contains non-linguistic features unique to a speaker and indicative of various speaker demographics, e.g. gender, nativity, ethnicity. Such characteristics are helpful cues for audio/video search and retrieval. In this paper, we evaluate the effects of various low-, mid-, and high-level features for effective classification of speaker characteristics. Low-level signal-based features include MFCCs, LPCs, and six spectral features; mid-level statistical features model lowlevel features; and high-level semantic features are based on selected phonemes in addition to mid-level features. Our data set consists of approximately 76.4 hours of annotated audio with 2786 unique speaker segments used for classification. Quantitative evaluation of our method results in accuracy rates up to 98.6% on our test data for male/female classification using mid-level features and a linear kernel support vector machine. We determine that mid- and high-level features are optimal for identification of speaker characteristics.

In this paper, we present extensive user studies on browsing and information retrieval in the domain of unstructured videos using the VAST MM video library browser. Our studies were performed over a 3-year period with more than 1,000 participants in the university setting. The majority of students use the video library for retrieval of student presentations in a large engineering design course. Through iterative analysis of context-specific audio, visual, and textual cues, we are able to measure significant improvements on typical retrieval tasks, such as searching for unfamiliar content in a large database with over 300 hours of video. We also present user studies conducted in two videotaped core computer science courses to measure the usefulness of the VAST MM (Video Audio Structure Text MultiMedia) resource for final exam preparation. We find that students who use the lecture video library experience significant improvement in final exam scores.To better compare video browsers featuring rich content cues to standard video players without cues, we have performed a large experiment to collect measurable data on search tasks. In general, the lack of index cues can be described by an inverse relationship between amount of matching video content and time required to find it. When index cues are available, the relationship is constant, that is, rare content is found in the same time as common content. We evaluate this data and provide additional insight into two common user interaction techniques: audio-visual browsing and visual-only browsing. We show that user preference is uniform, but that audio-visual browsing is significantly more effective for search and retrieval of video data.

Electric vehicles are not penetrating the market as quickly as expected. This is due to limited driving range, time required to recharge a battery, and lack of charging infrastructure in most metropolitan cities. We propose a charge sharing network in which we use inductive power transfer to wirelessly exchange charge between vehicles. In our network, vehicles that have excess charge to share, can sell charge to vehicles needing charge to reach their destination. In this paper, we describe a game theoretic approach to offering incentives for electric vehicles to participate in the charge sharing network. We utilize Nash Bargaining theory to show that participation in the network can yield profits for the seller driving to their destination and that we can increase the number of cars reaching their destination without needing to stop for recharging.