Faudziah Ahmad

A Competitive Study of Cryptography Techniques over Block Cipher

The complexity of cryptography does not allow many people to actually understand the motivations and therefore available for practicing security cryptography. Cryptography process seeks to distribute an estimation of basic cryptographic primitives across a number of confluences in order to reduce security assumptions on individual nodes, which establish a level of fault-tolerance opposing to the node alteration. In a progressively networked and distributed communications environment, there are more and more useful situations where the ability to distribute a computation between a number of unlike network intersections is needed. The reason back to the efficiency (separate nodes perform distinct tasks), fault-tolerance (if some nodes are unavailable then others can perform the task) and security (the trust required to perform the task is shared between nodes) that order differently. Hence, this paper aims to describe and review the different research that has done toward text encryption and description in the block cipher. Moreover, this paper suggests a cryptography model in the block cipher.

Designing a robot-assisted therapy for individuals with anxiety traits and states

The recent trend towards developing a new generation of robots capable of operating in human-centered environments, and participating in and assisting our daily lives has introduced the need for robotic systems capable to communicate and to react to their users in a social and engaging way. This type of robot could play essential roles to help individuals with severe cognitive problems. In this paper, several core components to design a robotic assisted therapy to support individuals with anxiety traits and states are presented.

A Multi-agent Model for Supporting Exchange Dynamics in Social Support Networks during Stress

Humans are social creatures, and when facing certain level of events, they to seek for support from others and vice versa. In this paper, a multi-agent model for simulating the dynamics of support provision and receipt interaction among different individuals is presented. Important concepts in social support network and stress buffering studies were used as the basis for model design and verification. Simulation experiments under several cases pointed out that the model is able to reproduce interaction among social support network members during stress. Mathematical analysis was conducted to determine possible equillibria of the model. The model was verified using an automated verification tool against generated traces.

An agent model for temporal dynamics analysis of a person with chronic fatigue syndrome

This paper presents a dynamic agent model of chronic fatigue syndrome for an individual. Chronic fatigue syndrome is the most common name to define a group of cognitive and medical disorders caused by persistent fatigue. Based on several personal characteristics, viral infection, and a representation of events (i.e. psychological and physiological stressors), the agent model can simulate whether a human agent that experience certain scenarios will fall into a chronic fatigue condition. A number of well-known relations between events and the course of chronic fatigue are summarized from the literature and it is shown that the model exhibits those patterns. In addition, the agent model has been mathematically analyzed to find out which stable situations exist. Finally, it is pointed out how this model can be used in therapy, supported by a software agent.

A subject identification method based on term frequency technique

The analyzing and extracting important information from a text document is crucial and has produced interest in the area of text mining and information retrieval. This process is used in order to notice particularly in the text. Furthermore, on view of the readers that people tend to read almost everything in text documents to find some specific information. However, reading a text document consumes time to complete and additional time to extract information. Thus, classifying text to a subject can guide a person to find relevant information. In this paper, a subject identification method which is based on term frequency to categorize groups of text into a particular subject is proposed. Since term frequency tends to ignore the semantics of a document, the term extraction algorithm is introduced for improving the result of the extracted relevant terms from the text. The evaluation of the extracted terms has shown that the proposed method is exceeded other extraction techniques.

Geometry based hybrid method for determining lesion area

In this paper, a hybrid method to measure lesion area on the green leaf surface is presented. The method involved combining two algorithms, Artificial Bee Colony and Otsu algorithm and applying geometry to obtain the area. Three leaf images were used that contains leaf lesions of different sizes: small, medium and big. The method was conducted in three phases, data preparation, algorithm construction and analysis. Comparison was made with pixel counting, and real measurement. The results show that the proposed hybrid method achieved results more accurate (average accuracy 92.03%) and faster (average time processing 0.015 milliseconds) than the pixel counting method.

Combining Sampling and Ensemble Classifier for Multiclass Imbalance Data Learning

The aim of this paper is to investigate the effects of combining various sampling and ensemble classifiers on the prediction performance in addressing the multiclass imbalance data learning. This research uses data obtained from the Malaysian medicinal leaf images shape data and three other large benchmark datasets in which seven ensemble methods from Weka machine learning tool were selected to perform the classification task. These ensemble methods include the AdaboostM1, Bagging, Decorate, END, MultiboostAB, RotationForest, and stacking methods. In addition to that, five base classifiers were used; Naive Bayes, SMO, J48, Random Forest, and Random Tree in order to examine the performance of the ensemble methods. Two methods of combining the sampling and ensemble classifiers were used which are called the Resample with ensemble classifier and SMOTE with ensemble classifier. The results obtained from the experiments show that there is actually no single configuration that is “one design that fits all”. However, it is proven that when using the sampling and ensemble classifier which is coupled with Random Forest, the prediction performance of the classification task can be improved on the multiclass imbalance dataset.

An Agent Model for Analysis of Trust Dynamics in Short-Term Human-Robot Interaction

Trust between human and robot is one of the crucial issues in robot-based therapy. It is highly important to provide a clearer and richer understanding and also to answer the questions why trust occurs in machines and how it can maintain successful interaction. In this paper, an agent based model for trust dynamics in short-term human-robot interaction is discussed and formally analysed. Three different cases were implemented to simulate various scenarios that explain the development of trust during short-term human-robot interaction; namely, (1) high level of trust, (2) moderate level of trust, and (3) low level of trust. Furthermore, simulation traces for fictional characters under different cases have pointed out realistic behaviours as existed in the literature. The developed model was verified by using mathematical (stability analysis) and automated verification (Temporal Trace Language).

An Ambient Agent Model for a Reading Companion Robot

This paper presents the development of an ambient agent model (software agent) as an initial step to develop a reading companion robot to sup-port reading performance. This ambient agent model provides detailed knowledge (human functioning) about reader’s dynamics states. Based on this human functioning knowledge, a robot will be able to reason about reader’s conditions and provides an appropriate support. Several simulation traces have been generated to illustrate the functioning of the proposed model. Furthermore, the model was verified using an automated trace analysis and the results have shown that the ambient agent model satisfies a number of related properties as presented in related literatures.

Formal Analysis of Temporal Dynamics in Anxiety States and Traits for Virtual Patients

This paper presents a temporal dynamic model of anxiety states and traits for an individual. Anxiety is a natural part of life, and most of us experience it from time to time. But for some people, anxiety can be extreme. Based on several personal characteristics, traits, and a representation of events (i.e. psychological and physiological stressors), the formal model can represent whether a human that experience certain scenarios will fall into an anxiety states condition. A number of well-known relations between events and the course of anxiety are summarized from the literature and it is shown that the model exhibits those patterns. In addition, the formal model has been mathematically analyzed to find out which stable situations exist. Finally, it is pointed out how this model can be used in therapy, supported by a software agent.