Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic signature verification system

Automatic signature verification system R. Malladi PhD thesis, Department of Computer Science, Faculty of Natural Sciences, University of the Western Cape. In this thesis, we explore dynamic signature verification systems. Unlike other signature models, we use genuine signatures in this project as they are more appropriate in real world applications. Signature verification systems are typical examples of biometric devices that use physical and behavioral characteristics to verify that a person really is who he or she claims to be. Other popular biometric examples include fingerprint scanners and hand geometry devices. Hand written signatures have been used for some time to endorse financial transactions and legal contracts although little or no verification of signatures is done. This sets it apart from the other biometrics as it is well accepted method of authentication. Until more recently, only hidden Markov models were used for model construction. Ongoing research on signature verification has revealed that more accurate results can be achieved by combining results of multiple models. We also proposed to use combinations of multiple single variate models instead of single multi variate models which are currently being adapted by many systems. Apart from these, the proposed system is an attractive way for making financial transactions more secure and authenticate electronic documents as it can be easily integrated into existing transaction procedures and electronic communications.
The Use of Edmodo to Improve the Writing Ability of English Department Students of State Polytechnic of Padang

This study aims to examine the usage of Edmodo for writing 1 subject for English Department students of State Polytechnic of Padang. The design of this research is experimental design; two classes became the experimental and control groups. There were 25 students in experimental group and 27 students in control group. Edmodo was used in experimental group while the control group was taught without using Edmodo. SPSS program was used to analyse the data. The result showed that the experimental group had improved significantly in their post test result. The paired samples statistics result showed that the mean of experiemental group post test was 70.32, while the control group post test was 63.94. For the t test result, the p value (0.000) was less than 0.05 for the experimental group, which means that the null hypothesis was rejected. On the other hand, the p value of the control group (0.06) was greater than 0.05 which means the null hypothesis was retained. From the statistic result, the study has proved that the use of Edmodo in writing class can improve students’ writing ability. Therefore, the researchers suggest other lecturers to use the device. However, further study may need to involve larger population in order to elucidate further the efficacy of this application. Keywords— Writing, Edmodo.
High performance reconfigurable architectures for bioinformatics and computational biology applications

The field of Bioinformatics and Computational Biology (BCB), a relatively new discipline which spans the boundaries of Biology, Computer Science and Engineering, aims to develop systems that help organise, store, retrieve and analyse genomic and other biological information in a convenient and speedy way. This new discipline emerged mainly as a result of the Human Genome project which succeeded in transcribing the complete DNA sequence of the human genome, hence making it possible to address many problems which were impossible to even contemplate before, with a plethora of applications including disease diagnosis, drug engineering, bio-material engineering and genetic engineering of plants and animals; all with a real impact on the quality of the life of ordinary individuals. Due to the sheer immensity of the data sets involved in BCB algorithms (often measured in tens/hundreds of Gigabytes) as well as their computation demands (often measured in TeraOps), high performance supercomputers and computer clusters have been used as implementation platforms for high performance BCB computing. However, the high cost as well as the lack of suitable programming interfaces for these platforms still impedes a wider undertaking of this technology in the BCB community. Moreover, with increased heat dissipation, supercomputers are now often augmented with special-purpose hardware (or ASICs) in order to speed up their operations while reducing their power dissipation. However, since ASICs are fully customised to implement particular tasks/algorithms, they suffer from increased development times, higher Non-Recurring-Engineering (NRE) costs, and inflexibility as they cannot be reused to implement tasks/algorithms other than those they have been designed to perform. On the other hand, Field Programmable Gate Arrays (FPGAs) have recently been proposed as a viable alternative implementation platform for BCB applications due to their flexible computing and memory architecture which gives them ASIC-like performance with the added programmability feature. In order to counter the aforementioned limitations of both supercomputers and ASICs, this research proposes the use of state-of-the-art reprogrammable system-on-chip technology, in the form of platform FPGAs, as a relatively low cost, high performance and reprogrammable implementation platform for BCB applications. This research project aims to develop a sophisticated library of FPGA architectures for bio-sequence analysis, phylogenetic analysis, and molecular dynamics simulation.
Improving IT Supported Organizational Change; Formalizing Organizational Implementation Fundamentals

External and internal changes require organizations to be agile. These changes often affect the implementation of an organization. Therefore, Op ’t Land and Krouwel proposed a set of organizational implementation variables(OIVs) to informedly decide upon the implementation of an organization. OIVs refer to the dimensions of the choices regarding the construction perspective of an enterprise, the implementation choices in the development of a system, given an agreed upon essence of the enterprise, and the varying nature of these choices. These OIVs are expected to have the potential to be supported by IT, but no research has been done to validate this. This research focused on formalizing the OIVs so that the support of the OIVs by IT can be assessed or designed. We revised the definitions of the OIVs and formalized them into one model. This model was validated by modeling an implementing of the fictional Rent-A-Car case. The impact of a reorganization at Rent-A-Car was analyzed to show the effects of changes that require agility. The formalization of the OIVs can be used to assess the extent to what an IT platform, or an enterprise information system supports the organizational implementation variables or to design IT platforms that can be used to build agile enterprise information systems. We are convinced that if the choices of the OIVs can easily be adapted in an organization, this enables continuous change of an enterprise with less effort in IT change.
Integrated vector instruction translator and offloading framework for mobile cloud computing / Junaid Shuja

Mobile Cloud Computing (MCC) facilitates energy efficient operations of mobile devices through computational offload. The mobile devices offload computations to nearby cloud servers while limiting energy consumption in the low-power wait mode. The MCC offloading frameworks are enabled by system virtualization, application virtualization, and native code migration techniques to address the heterogeneous computing architectures. The existing MCC offloading techniques suffer from either computational or communicational overheads leading to higher execution time and energy consumption on the cloud server. This research work addresses the overhead of conventional MCC offloading frameworks while focusing on vectorized applications based on Single Instruction Multiple Data (SIMD). We propose SIMDOM, a framework for SIMD instruction translation and offloading in heterogeneous MCC architectures. The SIMD translator utilizes re-compilation of SIMD instructions of the mobile device (ARM architecture) that are translated to corresponding cloud server instructions (x86 architecture). Based on inputs from the application, network, and mobile device energy profilers, the offloader module decides upon the feasibility of code offload. The SIMD translator is analyzed for its accuracy and translation overhead. The impact of code offload size, application partition, and device sleep time is investigated on the energy and time efficiency of the mobile applications. The lower feasibility bounds for server speed and application partition are derived from the system model. The SIMDOM framework prototype is implemented on a cloudlet and a cloud server. Results show that SIMDOM framework provides 85.66% energy and 3.93% time efficiency compared to MCC-disabled execution. Comparison with state-ofthe- art code offloading framework reveals that SIMDOM provides 55.99% energy and 57.30% time efficiency. The SIMDOM framework provides 31.10% higher energy efficiency while translating SIMD instructions as compared to existing MCC offloading frameworks. The improvement in energy and time efficiency increases the usability of MCC offloading frameworks for vectorized applications.
Efficient Matching Algorithms for Publish and Subscribe Systems

Publish/Subscribe is the paradigm in which users express long-term interests («subscriptions») and some external agent (perhaps other users) «publishes» events (e.g., offers). The job of Publish/Subscribe software is to send events to the owners of subscriptions satisfied by those events. For example, a user subscription may consist of an interest in an airplane of a certain type, not to exceed a certain price. A published event may consist of an offer of an airplane with certain properties including price. A subscriptio- n closely resembles a trigger in that it is a long-lived conditional query associated with an action (usually, informing the subscriber). However, it is less general than a trigger so novel data structures and implementations may enable the creation of scalable, high performance publish-subscribe systems. This paper describes an attempt at the construction of such algorithm- s and its implementation. Using a combination of data structures, application-- specific caching policies, and application-specific query processing our system can handle 600 events per second on 6 million subscriptions consisting of conjunctions of (attribute, comparison operator, value) predicates.
Content-based Image Understanding with Applications to Affective Computing and Person Recognition in Natural Settings

Chen Ming PhD, Purdue University, December 2016. Content-based Image Understanding with Applications to A↵ective Computing and Person Recognition in Natural Settings. Major Professor: Jan P. Allebach. Understanding the visual content of images is one of the most important topics in computer vision. Many researchers have tried to teach the machine to see and perceive like human. In this dissertation, we develop several new approaches for image understanding with applications to a↵ective computing, and person detection and recognition. Our proposed method applied to fashion photo analysis can understand the aesthetic quality of photos. Further, a bilinear model that takes into account the relative confidence of region proposals and the mutual relationship between multiple labels is developed to boost multi-label classification. It is evaluated both on object recognition and aesthetic attributes learning. We also develop a person detection and recognition system in natural settings that can robustly handle various pose, viewpoints, and lighting conditions. The system is then put into several real scenarios that has di↵erent amount of labelled data. Our algorithm that utilizes unlabelled data reduces the e↵ort needed for data annotation while achieving similar results as with labelled data.
Gaze-Based Control of Robot Arm in Three-Dimensional Space

Eye tracking technology has opened up a new communication channel for people with very restricted body movements. These devices had already been successfully applied as a human computer interface, e.g. for writing a text, or to control different devices like a wheelchair. This thesis proposes a Human Robot Interface (HRI) that enables the user to control a robot arm in 3-Dimensional space using only 2-Dimensional gaze direction and the states of the eyes. The introduced interface provides all required commands to translate, rotate, open or close the gripper with the definition of different control modes. In each mode, different commands are provided and direct gaze direction of the user is applied to generate continuous robot commands. To distinguish between natural inspection eye movements and the eye movements that intent to control the robot arm, dynamic command areas are proposed. The dynamic command areas are defined around the robot gripper and are updated with its movements. To provide a direct interaction of the user, gaze gestures and states of the eyes are used to switch between different control modes. For the purpose of this thesis, two versions of the above-introduced HRI were developed. In the first version of the HRI, only two simple gaze gestures and two states of the eye (closed eyes and eye winking) are used for switching. In the second version, instead of the two simple gestures, four complex gaze gestures were applied and the positions of the dynamic command areas were optimized. The complex gaze gestures enable the user to switch directly from initial mode to the desired control mode. These gestures are flexible and can be generated directly in the robot envir-
A Survey on the Result Based Analysis of Student Performance using Data Mining Techniques

–Extraction of information available in various data base repositories is a tedious task. A composed works of Data Mining (DM) method is accessible for different category of applications for the same work. Many researchers involve analyzing the student’s performance using some relevant DM techniques. This attracting little field is named as Learning Data Mining (LDM). The organizations of the syllabus also increase a very big contact about the growth of the student’s information and their performance. Among the different data mining methods, classification plays a vital role in learning data mining. The primary intention of this research work is to cross the data mining methods which are apply for the improvement of the student’s performance and also identify the most excellent appropriate structure of syllabus for the new environment. This study investigates about the use of ID3 and C4.5 classification algorithms for the improvement of student performance evaluation system. A comparative analysis of various works is carried out in this survey to identify the best classification algorithms for LDM.
Integration of artificial neural networks and simultion modeling in a decision support system

A simulation based decision support system is developed for AT&T Microelectronics in Orlando. This system uses simulation modeling to capture the complex nature of semiconductor test operations. Simulation, however, is not a tool for optimization by itself Numerous executions of the simulation model must generally be performed to narrow in on a set of proper decision parameters. As a means of alleviating this shortcoming, artificial neural networks are used in conjunction with simulation modeling to aid management in the decision making process. The integration of simulation and neural networks in a comprehensive decision support system, in effect, learns the reverse of the simulation process. That is, given a set of goals defined for performance measures, the decision support system suggests proper values for decision parameters to achieve those goals.
SmartWorkbench: Toward Adaptive and Transparent User Assistance in Industrial Human-Robot Applications

With the increase in customized production scenarios with small batch sizes research on human-robot working cells gains significance. We present a Smart Workbench that allows for adaptive user assistance in industrial human-robot use cases through (1) monitoring the user condition and (2) providing multi-modal information about the process and the robot intention. We further show results from a first study regarding the usefulness of biofeedback for workload estimation.
A GPU-based Framework for Real-time Free Viewpoint Television

Thesis addresses two main problems of Free Viewpoint TV: generation of arbitrary viewpoint in real-time and its delivery to end-user. For the ﬁrst problem a GPU-based algorithm capable of generating free viewpoints from a network of ﬁxed HD video cameras was developed. We used a space-sweep algorithm to estimate depth information. The view generation sub-system uses it to render new synthetic images for an arbitrary viewpoint. We show how the computations can be divided from each other and parallelized using CUDA. The tests for publicly available Microsoft sequence are provided. For the second problem, we proposed an open-source browser-based Free Viewpoint Video player. The player is implemented using WebGL where rendering is performed solely on a GPU. It is completely asynchronous - data retrieval from the server and rendering do not block the browser. The player ﬁts perfectly into the presented framework and can greatly improve scalability.
Semi-automatic identification of myelomatous lesions from multi modal MR images

Poster: "ECR 2018 / C-2274 / Semi-automatic identification of myelomatous lesions from multi modal MR images" by: "V. K. Anand1, G. KRISHNAMURTHI1, R. Balaji2; 1Chennai/IN, 2CHENNAI, TAMILNADU/IN"
Practical aspects of using a neural network to solve inverse geophysical problems

In this paper, an approach to solve an inverse problem of geophysics, such as determining the position of an object (cavity or cavern) and its geometrical parameters according to the propagation picture of a wave field, is proposed. At present there are no fast and accurate methods for determining such parameters. In this paper, a method based on neural networks (NNs) is proposed and a possible architecture of the NN is presented. The results of experiments on implementing and training the NN are also presented. The model obtained shows the presence of an "understanding" of the input data, demonstrating answers that are similar to the original data. In the NN answers, one can identify a relationship between the quality of the network response and the number of waves that have passed through the medium’s object being investigated.
A real-time fall detection system based on the acceleration sensor of smartphone

Falls are a major problem for the elderly people leading to injury, disability, and even death. In this article, we propose a fall detection system that uses the acceleration sensor of a smartphone to detect a senior citizen falling in real time and to use the communication capabilities of the smartphone to notify the administrator of such an event. The proposed system conducts real-time location tracking using Google’s Map and 3D information to cope with a critical situation and to make an urgent intervention in an emergency. Since Google’s 3D mapping service can provide information on the surrounding buildings and their shapes with more precision than a 2D service or text-based service, it is possible to ensure a prompt response for senior citizens when such location information is available. To verify the validity and efficiency of this article, we measured the change in acceleration sensor value by classifying the subjects by age group and gender. As a result, we found that the signal vector magnitude value of the acceleration sensor value variation showed a great difference in daily life such as walking, running, sitting, and falling.
Bluetooth Threat Taxonomy

Since its release in 1999, Bluetooth has become a commonly used technology available on billions of devices through the world. Bluetooth is a wireless technology used for information transfer by devices such as Smartphones, headsets, keyboard/mice, laptops/desktops, video game systems, automobiles, printers, heart monitors, and surveillance cameras. Dozens of threats have been developed by researchers and hackers which targets these Bluetooth enabled devices. The work in this thesis provides insight into past and current Bluetooth threats along with methods of threat mitigation. The main focus of this thesis is the Bluetooth Threat Taxonomy (BTT); it is designed for classifying threats against Bluetooth enabled technology. The BTT incorporates nine distinct classifications to categorize Bluetooth attack tools and methods and a discussion on 42 threats. In addition, several new threats developed by the author will be discussed. This research also provides means to secure Bluetooth enabled devices. The Bluetooth Attack Detection Engine (BLADE) is as a host-based Intrusion Detection System (IDS) presented to detect threats targeted toward a host system. Finally, a threat mitigation schema is provided to act as a guideline for securing Bluetooth enabled devices.
Fully automatic OWL generator from RDB schema

Use of ontologies in information systems and artificial intelligence has been emphasized in the recent years. Other than standardizing the vocabulary across a domain, ontologies enable the sharing of information between disparate systems within the same domain. Ontology engineers spend a lot of efforts in developing ontologies. A large amount of data on the Web is stored in the relational databases. In this paper, we have proposed and developed a tool that can fully automatically develop OWL ontology from a relational database. The main focus of our research is to develop a transformation process and to create rules for mappings between RDB and OWL constructs. Existing approaches have drawbacks that they are not fully automatic, performed mapping at a very basic level, outdated and are not easily accessible. In case of a large database, the existing tools fail to perform conversions efficiently. Our proposed tool is evaluated on different relational databases and can successfully perform the transformation with new mapping rules. Our tool is able to develop sub-data-properties and sub-classes which was never available before.
Position Control of an Unmanned Aerial Vehicle From a Mobile Ground Vehicle

The core objective of this research is to develop a closed loop feedback system between an unmanned aerial vehicle (UAV) and a mobile ground vehicle. With this closed loop system, the moving ground vehicle aims to navigate the aerial vehicle remotely. The ground vehicle uses a pure pursuit algorithm to traverse through a pre-defined path. A Proportional-Integral-Derivative (PID) controller is implemented for position control and attitude stabilization of the unmanned aerial vehicle. The problem of tracking and pose estimation of the UAV based on vision sensing is investigated. An estimator to track the states of the UAV using the images obtained from a single camera mounted on the ground vehicle is developed. This estimator coupled with a Kalman filter determines the UAV’s three dimensional position. The relative position of the UAV from the moving ground vehicle and control output from a joint centralized PID controller is used to further navigate the UAV and follow the motion of the ground vehicle in closed loop to avoid time delays. This closed loop system is simulated in MATLAB and Simulink to validate the proposed control and estimation approach. The results obtained validate the control architecture proposed to attain a closed loop feedback between the UAV and the mobile ground vehicle. UAV closely follows the ground vehicle and various simulation support the parameter settings.