Biblio

The interdisciplinary field of immersive learning research is scattered. Combining efforts for better exploration of this field from the different disciplines requires researchers to communicate and coordinate effectively. We call upon the community of immersive learning researchers for planting the Knowledge Tree of Immersive Learning Research, a proposal for a systematization effort for this field, combining both scholarly and practical knowledge, cultivating a robust and ever-growing knowledge base and methodological toolbox for immersive learning. This endeavor aims at promoting evidence-informed practice and guiding future research in the field. This paper contributes with the rationale for three objectives: 1) Developing common scientific terminology amidst the community of researchers; 2) Cultivating a common understanding of methodology, and 3) Advancing common use of theoretical approaches, frameworks, and models.

Poultry farming has contributed immensely to global food security and the economy. Its produces are favourites and hugely subscribed, due to the uniqueness of their nutrients to all categories of people and the alternatives they provide to other high-cholesterol proteins. The increase in the world's population will continuously stretch for an increase in demands for poultry products. A smart way to ensure continuous production and increased yields in various farms is to adopt automated and remote management of poultries. This paper modelled and developed a collaborative system using the synergistic wireless sensor network technology and the internet of things. The system integrated resourcefully selected wireless sensors, mobile phone, other autonomous devices and the internet to remotely monitor and control environmental parameters and activities within the farm. Parameters such as temperature, humidity, water level, food valve level, ammonia gas, illumination are sensed, benchmarked against selected thresholds, and communicated wirelessly to the sink node and the internet cloud. The required control actions can also be initiated remotely by the administrator through messages or command signal. Also, the various parameters and actions can be read or documented in real-time over the web. The system was tested and evaluated to give an average of about 93.7% accuracy in parameters detection and 2s delay in real-time response. Therefore, a modelled system has been developed to provide robust and more intuitive solutions in poultry farming.

The world is fundamentally compositional, so it is natural to think of visual recognition as the recognition of basic visually primitives that are composed according to well-defined rules. This strategy allows us to recognize unseen complex concepts from simple visual primitives. However, the current trend in visual recognition follows a data greedy approach where huge amounts of data are required to learn models for any desired visual concept. In this paper, we build on the compositionality principle and develop an "algebra" to compose classifiers for complex visual concepts. To this end, we learn neural network modules to perform boolean algebra operations on simple visual classifiers. Since these modules form a complete functional set, a classifier for any complex visual concept defined as a boolean expression of primitives can be obtained by recursively applying the learned modules, even if we do not have a single training sample. As our experiments show, using such a framework, we can compose classifiers for complex visual concepts outperforming standard baselines on two well-known visual recognition benchmarks. Finally, we present a qualitative analysis of our method and its properties.

In current, RFID (Radio Frequency Identification) Middleware is widely used in nearly all RFID applications, and provides service for raw data capturing, security data reading/writing as well as sensors controlling. However, as the existing Middlewares were organized with rigorous data comparison and data encryption, it is seriously affecting the usefulness and execution efficiency. Thus, in order to improve the utilization rate of effective data, increase the reading/writing speed as well as preserving the security of RFID, this paper proposed a PSO (Particle swarm optimization) clustering scheme to accelerate the procedure of data operation. Then with the help of PSO cluster, the RFID Middleware can provide better service for both data security and data availability. At last, a comparative experiment is proposed, which is used to further verify the advantage of our proposed scheme.

The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNN-based architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset.

There are two types of network architectures are presents those are wired network and wireless network. MANETs is one of the examples of wireless network. Each and every network has their own features which make them different from other types of network. Some of the features of MANETs are; infrastructure less network, mobility, dynamic network topology which make it different and more popular from wired network but these features also generate different problems for achieving security due to the absence of centralized authority inside network as well as sending of data due to its mobility features. Achieving security in wired network is little-bit easy compare to MANETs because in wired network user need to just protect main centralized authority for achieving security whereas in MANETs there is no centralized authority available so protecting server in MANETs is difficult compare to wired network. Data sending and receiving process is also easy in wired network but mobility features makes this data sending and receiving process difficult in MANETs. Protecting server or central repository without making use of secrete sharing in wired network will create so many challenges and problem in terms of security. The proposed system makes use of Secrete sharing method to protect server from malicious nodes and `A New particle Swarm Optimization Method for MANETs' (NPSOM) for performing data sending and receiving operation in optimization way. NPSOM technique get equated with the steady particle swarm optimizer (PSO) technique. PSO was essentially designed by Kennedy, Eberhart in 1995. These methods are based upon 4 dissimilar types of parameters. These techniques were encouraged by common performance of animals, some of them are bird assembling and fish tuition, ant colony. The proposed system converts this PSO in the form of MANETs where Particle is nothing but the nodes in the network, Swarm means collection of multiple nodes and Optimization means finding the best and nearer root to reach to destination. Each and every element study about their own previous best solution which they are having with them for the given optimization problem, likewise they see for the groups previous best solution which they got for the same problem and finally they correct its solution depending on these values. This same process gets repeated for finding of the best and optimal solutions value. NPSOM technique, used in proposed system there every element changes its location according to the solution which they got previously and which is poorest as well as their collection's earlier poorest solution for finding best, optimal value. In this proposed system we are concentrating on, sidestepping element's and collections poorest solution which they got before.

CAPTCHA is a type of challenge-response test to ensure that the response is only generated by humans and not by computerized robots. CAPTCHA are getting harder as because usage of latest advanced pattern recognition and machine learning algorithms are capable of solving simpler CAPTCHA. However, some enhancement procedures make the CAPTCHAs too difficult to be recognized by the human. This paper resolves the problem by next generation human-friendly mini game-CAPTCHA for quantifying the usability of CAPTCHAs.