Abstract: The purpose of this study is to investigate pre-service teachers’ technological, pedagogical and content knowledge (TPACK) in Turkey. By using the “Survey of Pre-service Teachers’ Knowledge of Teaching and Technology” developed by Schmidt et al. (2009), the study sought to determine if significant differences could be found in pre-service teachers’ perceptions of TPACK when examined by gender, age, educational program, year of study, kind of instruction (day or night education) and field experience. Regression analysis was also used to examine if technology knowledge (TK), pedagogical knowledge (PK) and content knowledge (CK) significantly contributed to pre-service teachers’ TPACK development. Participants of this study were 491 elementary pre-service teachers who attended the summer semester at Pamukkale University. The analysis of the collected data found a significant difference in pre-service teachers’ perceptions of the TPACK when examined across gender, program, year of study and field experience, but no significant differences were found regarding age and kind of instruction. Finally, our regression model showed that CK and PK contributed significantly to pre-service teachers’ TPACK development, but TK was not a significant predictor.
Abstract: Since wireless sensor networks (WSNs) have been designed to be deployed in an unsecured, public environment, secured communication is really vital for their wide-spread use. Among all of the communication protocols developed for WSN, the Security Protocols for Sensor Networks (SPINS) is exceptional, as it has been designed with security as a goal. SPINS is composed of two building blocks: Secure Network Encryption Protocol (SNEP) and the “micro” version of the Timed Efficient Streaming Loss-tolerant Authentication (TESLA), named μTESLA. From the inception of SPINS, a number of efforts have been made to validate its security properties. In this paper, we have validated the security properties of SNEP by using an automated security protocol validation tool, named AVISPA. Using the protocol specification language, HLPSL, we model two combined scenarios—node to node key agreement and counter exchange protocols—followed by data transmission. Next, we validate the security properties of these combined protocols, using different AVISPA back-ends. AVISPA reports the models we have developed free from attacks. However, by analyzing the key distribution sub-protocol, we find one threat of a potential DoS attack that we have demonstrated by modeling in AVISPA. Finally, we propose a modification, and AVISPA reports this modified version free from the potential DoS attack.
Abstract: Mobile dispatching applications have become popular for at least two major reasons. The first reason is a more mobile-centric usage pattern, where users relate to apps for fulfilling different needs that they have. In this respect, a vehicle dispatching application for mobile phones is perceived as a modern way of booking a vehicle. The second reason has to do with the advantages that this method has over traditional dispatching systems, such as being able to see the vehicle approaching on a map, being able to rate a driver and the most importantly spurring customer retention. The taxi dispatching business, one of the classes of dispatching businesses, tends to be a medium to lower class fidelity service, where users mostly consider the closest taxi as opposed to quality, which is regarded as being at a relatively consistent level. We propose a new approach for the taxi ordering application , a mobile dispatching system, which allows for a more engaged user base and offers fidelity rewards that are used to enhance the customer retention level based on a built in social customer relationship management (CRM) component. With this approach, we argue that in a business world which is shifting from a consumer-centric marketing to a human-centric model, this apps will allows taxi businesses to better interact with their clients in a more direct and responsible manner. Also this distributed system helps taxi drivers, which can receive orders directly from their clients and will be able to benefit from offering quality services as they can get higher ratings.
Abstract: As smartphones become widespread, a variety of smartphone applications are being developed. This paper proposes a method for indoor localization (i.e., positioning) that uses only smartphones, which are general-purpose mobile terminals, as reference point devices. This method has the following features: (a) the localization system is built with smartphones whose movements are confined to respective limited areas. No fixed reference point devices are used; (b) the method does not depend on the wireless performance of smartphones and does not require information about the propagation characteristics of the radio waves sent from reference point devices, and (c) the method determines the location at the application layer, at which location information can be easily incorporated into high-level services. We have evaluated the level of localization accuracy of the proposed method by building a software emulator that modeled an underground shopping mall. We have confirmed that the determined location is within a small area in which the user can find target objects visually.
Abstract: Image labeling tools help to extract objects within images to be used as ground truth for learning and testing in object detection processes. The inputs for such tools are usually RGB images. However with new widely available low-cost sensors like Microsoft Kinect it is possible to use depth images in addition to RGB images. Despite many existing powerful tools for image labeling, there is a need for RGB-depth adapted tools. We present a new interactive labeling tool that partially automates image labeling, with two major contributions. First, the method extends the concept of image segmentation from RGB to RGB-depth using Fuzzy C-Means clustering, connected component labeling and superpixels, and generates bounding pixels to extract the desired objects. Second, it minimizes the interaction time needed for object extraction by doing an efficient segmentation in RGB-depth space. Very few clicks are needed for the entire procedure compared to existing, tools. When the desired object is the closest object to the camera, which is often the case in robotics applications, no clicks at all are required to accurately extract the object.
Abstract: In this paper, error resilience is achieved by adaptive, application-layer rateless channel coding, which is used to protect H.264/Advanced Video Coding (AVC) codec data-partitioned videos. A packetization strategy is an effective tool to control error rates and, in the paper, source-coded data partitioning serves to allocate smaller packets to more important compressed video data. The scheme for doing this is applied to real-time streaming across a broadband wireless link. The advantages of rateless code rate adaptivity are then demonstrated in the paper. Because the data partitions of a video slice are each assigned to different network packets, in congestion-prone wireless networks the increased number of packets per slice and their size disparity may increase the packet loss rate from buffer overflows. As a form of congestion resilience, this paper recommends packet-size dependent scheduling as a relatively simple way of alleviating the buffer-overflow problem arising from data-partitioned packets. The paper also contributes an analysis of data partitioning and packet sizes as a prelude to considering scheduling regimes. The combination of adaptive channel coding and prioritized packetization for error resilience with packet-size dependent packet scheduling results in a robust streaming scheme specialized for broadband wireless and real-time streaming applications such as video conferencing, video telephony, and telemedicine.