Embedding 3-D Gaze Points on a 3-D Visual Field: A Case of Transparency

The paper seeks to demonstrates the likelihood of embedding a 3D gaze point on a 3D visual field, the visual field is inform of a game console where the user has to play from one level to the other by overcoming obstacles that will lead them to the next level. Complex game interface is sometimes difficult for the player to progress to next level of the game and the developers also find it difficult to regulate the game for an average player. The model serves as an analytical tool for game adaptations and also players can track their response to the game. Custom eye tracking and 3D object tracking algorithms were developed to enhance the analysis of the procedure. This is a part of the contributions to user interface design in the aspect of visual transparency. The development and testing of human computer interaction uses and application is more easily investigated than ever, part of the contribution to this is the embedding of 3-D gaze point on a 3-D visual field. This could be used in a number of applications, for instance in medical applications that includes long and short sightedness diagnosis and treatment. Experiments and Test were conducted on five different episodes of user attributes, result show that fixation points and pupil changes are the two most likely user attributes that contributes most significantly in the performance of the custom eye tracking algorithm the study. As the advancement in development of eye movement algorithm continues user attributes that showed the least likely appearance will prove to be redundant.

Efficient Authentication Algorithm for Secure Remote Access in Wireless Sensor Networks

Wireless sensor networks convey mission critical data that calls for adequate privacy and security protection. To accomplish this objective, numerous intrusion detection schemes based on machine learning approaches have been developed. In addition, authentication and key agreements techniques have been developed using techniques such as elliptic curve cryptography, bilinear pairing operations, biometrics, fuzzy verifier and Rabin cryptosystems. However, these schemes have either high false positive rates, high communication, computation, storage or energy requirements, all of which are not ideal for battery powered sensor nodes. Moreover, majority of these algorithms still have some security and privacy challenges that render them susceptible to various threats. In this paper, a WSN authentication algorithm is presented that is shown to be robust against legacy WSN privacy and security attacks such as sidechannel, traceability, offline guessing, replay and impersonations. From a performance perspective, the proposed algorithm requires the least computation overheads and average computation costs among its peers.

Natural Language Processing and Its Challenges on Omotic Language Group of Ethiopia

This article reviews Natural Language Processing (NLP) and its challenge on Omotic language groups. All technological achievements are partially fuelled by the recent developments in NLP. NPL is one of component of an artificial intelligence (AI) and offers the facility to the companies that need to analyze their reliable business data. However, there are many challenges that tackle the effectiveness of NLP applications on Omotic language groups (Ometo) of Ethiopia. These challenges are irregularity of the words, stop word identification problem, compounding and languages ‘digital data resource limitation. Thus, this study opens the room to the upcoming researchers to further investigate the NLP application on these language groups.

Enhanced Information Systems Success Model for Patient Information Assurance

The current health information systems have many challenges such as lack of standard user interfaces, data security and privacy issues, inability to uniquely identify patients across multiple hospital information systems, probable misuse of patient data, high technological costs, resistance to technology deployments in hospital management, lack of data gathering, processing and analysis standardization. All these challenges, among others hamper either the acceptance of the health information systems, operational efficiency or expose patient information to cyber attacks. In this paper, an enhanced information systems success model for patient information assurance is developed using an amalgamation of Technology Acceptance Model (TAM) and Information Systems Success Model (ISS). This involved the usage of Linear Structured Relationship (LISREL) software to model a combination of ISS and Intention to Use (ITU), TAM and ITU, ISS and user satisfaction (US), and finally TAM and US. The sample size of 110 respondents was obtained based on the total population of 221 using the Conhrans formula. Thereafter, simple random sampling was employed to select members within each category of employees to take part in the study. The questionnaire as a research tool was checked for reliability via Cronbach’s Alpha. The results obtained showed that for ISS and ITU modeling, only perceived ease of use, system features, response time, flexibility, timeliness, accuracy, responsiveness and user training positively influenced the intention to use. However, for the TAM and ITU modeling, only TAM’s measures such as timely information, efficiency, increased transparency, and proper patient identification had a positive effect on intension to use. The ISS and US modeling revealed that perceived ease of use had the greatest impact on user satisfaction while response time had the least effect on user satisfaction. On its part, the TAM and US modeling showed that timely information, effectiveness, consistency, enhanced communication, and proper patients identification had a positive influence on user satisfaction.

Comparative Analysis of Scheduling Algorithms Performance in a Long Term Evolution Network

The advancement in cellular communications has enhanced the special attention given to the study of resource allocation schemes. This study is to enhance communications to attain efficiency and thereby offers fairness to all users in the face of congestion experienced anytime a new product is rolled out. The comparative analysis was done on the performance of Enhanced Proportional Fair, Qos-Aware Proportional Fair and Logarithmic rule scheduling algorithms in Long Term Evolution in this work. These algorithms were simulated using LTE system toolbox in MATLAB and their performances were compared using Throughput, Packet delay and Packet Loss Ratio. The results showed Qos-Aware Proportional Fair has a better performance in all the metrics used for the evaluation.

Quick Quantum Circuit Simulation

Quick Quantum Circuit Simulation (QQCS) is a software system for computing the result of a quantum circuit using a notation that derives directly from the circuit, expressed in a single input line. Quantum circuits begin with an initial quantum state of one or more qubits, which are the quantum analog to classical bits. The initial state is modified by a sequence of quantum gates, quantum machine language instructions, to get the final state. Measurements are made of the final state and displayed as a classical binary result. Measurements are postponed to the end of the circuit because a quantum state collapses when measured and produces probabilistic results, a consequence of quantum uncertainty. A circuit may be run many times on a quantum computer to refine the probabilistic result. Mathematically, quantum states are 2n -dimensional vectors over the complex number field, where n is the number of qubits. A gate is a 2n ×2n unitary matrix of complex values. Matrix multiplication models the application of a gate to a quantum state. QQCS is a mathematical rendering of each step of a quantum algorithm represented as a circuit, and as such, can present a trace of the quantum state of the circuit after each gate, compute gate equivalents for each circuit step, and perform measurements at any point in the circuit without state collapse. Output displays are in vector coefficients or Dirac bra-ket notation. It is an easy-to-use educational tool for students new to quantum computing.

Emoji Essence: Detecting User Emotional Response on Visual Centre Field with Emoticons

User experience is understood in so many ways, like a one on one interaction (subjective views), online surveys and questionnaires. This is simply so get the user’s implicit response, this paper demonstrates the underlying user emotion on a particular interface such as the webpage visual content based on the context of familiarisation to convey users’ emotion on the interface using emoji, we integrated physiological readings and eye movement behaviour to convey user emotion on the visual centre field of a web interface. The physiological reading is synchronised with the eye tracker to obtain correlating user interaction, and emoticons are used as a form of emotion conveyance on the interface. The eye movement prediction is obtained through a control system’s loop and is represented by different color display of gaze points (GT) that detects a particular user’s emotion on the webpage interface. These are interpreted by the emoticons. Result shows synchronised readings which correlates to area of interests (AOI) of the webpage and user emotion. These are prototypical instances of authentic user response execution for a computer interface and to easily identify user response without user subjective response for better and easy design decisions.

Intrusion Detection through DCSYS Propagation Compared to Auto-encoders

In network settings, one of the major disadvantages that threaten the network protocols is the insecurity. In most cases, unscrupulous people or bad actors can access information through unsecured connections by planting software or what we call malicious software otherwise anomalies. The presence of anomalies is also one of the disadvantages, internet users are constantly plagued by virus on their system and get activated when a harmless link is clicked on, this a case of true benign detected as false. Deep learning is very adept at dealing with such cases, but sometimes it has its own faults when dealing benign cases. Here we tend to adopt a dynamic control system (DCSYS) that addresses data packets based on benign scenario to truly report on false benign and exclude anomalies. Its performance is compared with artificial neural network auto-encoders to define its predictive power. Results show that though physical systems can adapt securely, it can be used for network data packets to identify true benign cases.

Web Application Authentication Using Visual Cryptography and Cued Clicked Point Recall-based Graphical Password

Alphanumerical usernames and passwords are the most used computer authentication technique. This approach has been found to have a number of disadvantages. Users, for example, frequently choose passwords that are simple to guess. On the other side, if a password is difficult to guess, it is also difficult to remember. Graphical passwords have been proposed in the literature as a potential alternative to alphanumerical passwords, based on the fact that people remember pictures better than text. Existing graphical passwords, on the other hand, are vulnerable to a shoulder surfing assault. To address this shoulder surfing vulnerability, this study proposes an authentication system for web-applications based on visual cryptography and cued click point recall-based graphical password. The efficiency of the proposed system was validated using unit, system and usability testing measures. The results of the system and unit testing showed that the proposed system accomplished its objectives and requirements. The results of the usability test showed that the proposed system is easy to use, friendly and highly secured.

Student Performance Prediction Using A Cascaded Bi-level Feature Selection Approach

Features in educational data are ambiguous which leads to noisy features and curse of dimensionality problems. These problems are solved via feature selection. There are existing models for features selection. These models were created using either a single-level embedded, wrapperbased or filter-based methods. However single-level filter-based methods ignore feature dependencies and ignore the interaction with the classifier. The embedded and wrapper based feature selection methods interact with the classifier, but they can only select the optimal subset for a particular classifier. So their selected features may be worse for other classifiers. Hence this research proposes a robust Cascade Bi-Level (CBL) feature selection technique for student performance prediction that will minimize the limitations of using a single-level technique. The proposed CBL feature selection technique consists of the Relief technique at first-level and the Particle Swarm Optimization (PSO) at the second-level. The proposed technique was evaluated using the UCI student performance dataset. In comparison with the performance of the single-level feature selection technique the proposed technique achieved an accuracy of 94.94% which was better than the values achieved by the single-level PSO with an accuracy of 93.67% for the binary classification task. These results show that CBL can effectively predict student performance.