The present study evaluated preference for choice and its effect on motivation during response acquisition of receptive identification targets in children diagnosed with autism. Prior to the study a multiple stimulus without replacement preference assessment was conducted in order to determine the highly preferred stimulus. Choice was evaluated during a high-preferred, free-choice, and control condition for each participant. During the high-preferred condition, participants were only able to work for the high-preferred stimulus identified during the MSWO. During free choice, participants could choose among the five items presented in the MSWO, and during control each participant was unable to exchange his or her tokens for reinforcement. Choice was also evaluated during a concurrent-chain arrangement, where each participant was able to choose the condition he or she wanted to work under. The results showed that two out of the three participants did not show differentiation during the acquisition phase; however, during the concurrent-chain phase, all three participants demonstrated a stronger preference for free choice.

Previous research on implementer preference for behavioral interventions is limited. This study empirically evaluated teachers’ preference for implementing two types of token economies in a classroom to increase on-task behavior of students. The two types of token economies were response cost and differential reinforcement of alternative behavior implemented using an interdependent group contingency. The results showed both teachers preferred response cost over differential reinforcement of alternative behavior.

Idag finns det m˚anga system f¨or att bed¨oma och tolka ¨overs¨attningar av texter. Det finns system som l¨ankar delar av en k¨alltext och en ¨overs¨attning, det finns en ¨aven tekniker f¨or att bed¨oma ¨overs¨attningar f¨or ge ett m˚att p˚a hur bra de ¨ar. Ett exempel p˚a en s˚adan teknik ¨ar Token Equivalence Method(TEM). Det finns dock f˚a program, om n˚agra, som utnyttjar b˚ade l¨ankning och bed¨omning p˚a ett s˚adant s¨att att de skulle kunna vara anv¨andbara vid till exempel spr˚akutbildningar. I detta arbete utvecklas just ett s˚adant program. Programmet som skapats kan segmentera och l¨anka parallella texter mot varandra helt automatiskt via inkopplade system. F¨or att ¨oka anv¨andarv¨anligheten s˚a visualiserar programmet ¨aven l¨ankningen och till˚ater redigering av b˚ade segmentering och l¨ankning. L¨ankningen utnyttjas sedan f¨or att r¨akna ut och visa delar av TEM f¨or att ge ett m˚att p˚a ¨overs¨attningens kvalit´e.

The Collaborative Authentication (co-authentication) system is an authentication system that relies on some or all members of a pre-registered set of secure hardware tokens being concurrently present to an authentication server at the moment of authentication. Previous researchers have compared various embodiments of the co-authentication system to each other including using Quick Response (QR) codes/cellphone cameras and Near Field Communication (NFC) between tokens. This thesis concerns the initial design and implementation of empirical comparative testing mechanisms between one embodiment of the co-authentication system and other commonly used authentication systems. One contribution is the simulated standard user ID and password login in a computer browser and a simulated RSA SecureID ® one time password (OTP) and login with embedded usability testing mechanisms. Another contribution is the development and implementation of a new Bluetooth communication functionality between tokens. A third contribution is the addition of usability testing mechanisms to two versions of this new functionality.

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Microblog has become a central platform where people express their thoughts and opinions toward public events in China. With the sudden outbreak of coronavirus, the posts related to coronavirus are usually followed by a burst immediately in microblog volume, which provides a great opportunity to explore public sentiment about the events. In this context, sentiment analysis is helpful to explore how coronavirus affects public opinions. Deep learning has become a very popular technique for sentiment analysis. This thesis uses Bidirectional Encoder Representations from Transformers (BERT), a pre-trained unsupervised language representation model based on deep learning, to generate initial token embeddings that are further tuned by a neural network model on a supervised corpus, a sentiment classifier is constructed. We utilize data recently made available by the government of Beijing which contains 1 million blog posts from January 1 to February 20, 2020. Also, the model developed in this thesis can be used to track the sentiment variation with Weibo microblog data in the future. At the final stage, the variation of public sentiment is analyzed and presented with visualization charts of preformed people sentiment variation with the development of coronavirus in China. Comparison of the results between labeled data and all data is performed in order to explore how thoughts and opinions evolve in time. The result shows a significant growth of the negative sentiment on January 20 when a lockdown started in Wuhan, and afterward the growth becomes slower. Around February 7 when doctor Wenliang Li died, the number of negative sentiments reached its peak.

The aim of this diploma thesis was to create an own DiffServ domain model, which is supplemented with stations generating various type of network traffic and implementation of token-bucket mechanism in router´s process model in Opnet Modeler simulation environment. DiffServ domain is made up from two edge and two core routers and includes servers and client stations generating VoIP, FTP, HTTP and database access traffic. It is described a process of distribution of traffic into different classes on edge routers of DiffServ domain along with assurance of separate handling with usage of an Assured Forwarding PHB mechanism. In point of differentiated packet processing is process model completed with generating of various statistics. The process of their creating is used to check a dropper activity, which is implemented together with token-bucket mechanism on ARP layer of the router. The functionality of the model is verified by simulation.

This Master’s thesis is dealt in the local area network security, cryptographic algorithms, particular attacks on computer networks a practical application these attacks in local area networks. To application particular attacks is used the Cain & Abel program. The detailed manual for this program is created from the results of these attacks. This manual contains the exhibits of usage particular program tools and the attack application exhibits. This manual considers consequences of particular attacks and summarises achieved results during work with tools too. Master thesis closely deals with one of the program tools called RSA SecureID Token Calculator. Authentication via hardware tokens is contained in this Master thesis. Thesis contains the way of authentication using RSA SecureID Token Calculator without physical owning of the hardware token. Cain & Abel program shows and interprets why cashed passwords in operation system are dangerous and it shows methods how attacker can reveal this passwords from the operation system memory. This Master thesis is focused on sniffing credentials and passwords in local area networks and it is focused on cryptographic algorithms cracking for username and passwords revealing.

There is some discrepancy on how differential reinforcement of other behavior (DRO) and response cost (RC) affect responding within the context of token economies. We evaluated DRO and two variations of response cost with and without verbal feedback to decrease response errors in the context of academic skills for three children diagnosed with Down syndrome. The results suggest that a token economy intervention alone, with or without verbal feedback, was not effective for 2 of 3 participants. Gestural prompting was required to reduce response errors for those 2 participants and is possibly responsible for reducing response errors for the third participant.

As the obesity rate in America continues to rise, the levels of physical activity have persistently declined at a rapid pace across all age groups. This trend is demonstrated most significantly in individuals diagnosed with intellectual disabilities (ID). Due to the high obesity rate in individuals with ID, it is crucial to find an effective intervention to increase physical activity. The purpose of this study is to compare the effectiveness of token reinforcement and monetary reinforcement for increasing physical activity among adults with ID, to assess preference for token or monetary reinforcement, and to evaluate the effects of choice of reinforcement procedure on physical activity. An ABAB design with an alternating treatments design was used to compare the two conditions (token reinforcement and monetary reinforcement). In the second intervention phase, the participants chose between the two reinforcement conditions. Results showed that both reinforcement conditions increased physical activity.