Miracle-dz/UniFAR-FTUs huggingface dataset:
Category-level object detection and image classification

Automatic recognition of object categories from complex real-world images is an exciting problem in computer vision. While humans can perform recognition tasks effortlessly and proficiently, replicating this recognition ability of humans in machines is still an incredibly difficult problem. On the other hand, successful automatic recognition technology will have significant and mostly positive impact in a plethora of important application domains like image retrieval, visual surveillance and automotive safety systems. This dissertation addresses two main goals of recognition: image classification and object detection. Image classification seeks to separate images which contain an object category from other images, where the focus is on identifying the presence or absence of an object category in an image. Object detection concerns the identification and localization of object instances of a category in an image, where the goal is to localize all instances of that category from the image. Our main contributions toward this end are threefold. Firstly, we develop a novel and powerful method to detect ellipses from edge images. Our method specifically addresses the structural issues related to broken edge maps, background clutter and partial occlusion. Additionally, we incorporate a self-correcting mechanism into the ellipse detector which empowers it with an ability to identify weak ellipses and to regenerate new ellipses that better represent edge information. Experimental evaluation on complex synthetic and real images shows our ellipse detection method to have systematic and substantial improvements over previous methods. We are unaware of any other works that can detect ellipses from such difficult images. Consequently, the proposed method advances the state-of-the-art in ellipse detection. ii ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library
Underwater 3D Reconstruction Based on Geometric Transformation of Sonar and Depth Information

3D reconstruction is of vital importance to detect and monitor the underwater environment. A method based on geometric transformation of mechanical scanning sonar and depth information is proposed, in which the point cloud data from sonar and depth gauge are acquired to reconstruct the underwater 3D environment. However, noise and interference can affect the measurement of sonar, and movement of sonar during measurement can lead to distortion of the received data. Meanwhile, translation and rotation movement of sonar head may happen when ROV dives which can lead to different body reference coordinates of different scanning. To solve this, pre-processing and motion compensation are implemented at first, and underwater matching correction algorithm is used to calculate the translation and rotation of the sonar head. Then the inverse operation is implemented to convert the scan data of every depth into the same coordinate reference system. Finally, surface reconstruction of point clouds from sonar the depth information are used to reconstruct underwater environment based on MLS (Moving Least Square Method) using PCL (Point Cloud Library). Water tank experiments verify the effectiveness of the proposed method.
Extending the hidden Markov model for activity scheduling

An extension of the Hidden Markov Model is proposed for the task of capturing the underlying distribution of activity chains, activity durations and activity start times as reported in the household travel surveys. Such a model can derive more accurate activity schedules for a synthetic population. A sample of 1 million agents was generated for a 24-h typical day and compared against the Singapore Household Travel Survey showing an accurate match for the statistics evaluated.
The role of rare codons in protein expression

That the flow of information from gene sequence to protein sequence depends on the translation of a code that could literally be described as digital is a truly incredible feat of nature. However, the process of translation is a noisy, stochastic, kinetic process that depends on many factors. The redundancy in the genetic code allows the transmission of additional, analogue information by varying some of these factors. How organisms use the redundancy is termed codon usage, and rare codons are those that are typically shunned in favour of other synonymous options. Synonymous variations to the codon usage pattern of a gene have been linked to disease, and can have huge effects on the functionality and quantity of protein produced from a gene, but the nature of these variations is complex and poorly understood. In some cases, rare codons appear to have a beneficial influence on expression. This thesis investigates the phenomenon of rare codons and attempts to elucidate their evolutionary role in optimal gene expression. It begins with the design of a novel statistical algorithm, which is used to generate a dataset of interesting genetic locations. The dataset is the subject of a hypothesis-driven investigation to discover meaningful biological correlates, and this is complemented by experimental work, to attempt to provide conclusive validation of the approach.
Creating adaptive web recommendation system based on user behavior

The paper proposes adaptive web recommendation system based on user behavior. The proposed system uses expert system to evaluating and recommending suitable items of content. Relevant items are subsequently evaluated and filtered based on history of visited items and user´s preferred categories of items. Main parts of the proposed system are presented and described. The proposed recommendation system is verified on specific example.
Energy Aware Issues of DSR Routing Protocols in Mobile Ad Hoc Network

Dynamic source routing protocol (DSR) is an on demand routing protocol that is deemed a suited reactive routing protocol for MANET. Mobile ad hoc network is composed of independent devices communicating with each other directly without the help of any constant infrastructure or centralized administration.In general some nodes in network cut off working because battery run out of power, therefore it is much important to preserve the energy. DSR suffers from energy depletion and that because DSR routing protocol doesn’t take the term of energy consumption as a parameter into account at all. DSR routing protocol has main advantages to supply path and transmit data packets from source to destination but its disadvantage is transition energy which is forfeiture when the data packets are drop or when the acknowledgement not receive by neighbor nodes that time these paths not efficient for data
Application of unmanned aerial vehicle multi-vision image 3D modeling in geological disasters

Abstract Frequent geological disasters in Yunnan province, southwest China, threaten the personal safety of local residents and the operational safety of construction projects. Multivision images of these areas can be quickly acquired using unmanned aerial vehicles (UAVs). From such images, a three-dimensional (3D) geographic information model can be quickly obtained using data processing. These models are useful for analyzing geological disasters. The present paper focuses on methods and key technologies for producing 3D models from UAV multi-vision images. Results showed that feature–grayscale matching adapted to image deformation and assured the matching precision. Finally, 3D models were constructed from two groups of image data. Compared with conventional imaging results, such as DEM, DOM and DLG, the 3D model was more precise, intuitive and accurate. Thus, geological workers can more clearly evaluate the geological environment and effectively predict and control geological disasters.
Low-Power and Error-Resilient VLSI Circuits and Systems.

Low-Power and Error-Resilient VLSI Circuits and Systems by Chia-Hsiang Chen Chair: Zhengya Zhang Efficient low-power computation is critically important for the success of the nextgeneration very-large-scale integration (VLSI) applications. Device and supply voltage (Vcc) have been continuously pushed smaller and lower to meet a more constrained power envelope with each generation, but device and voltage scaling have created resiliency challenges, including increasing timing faults, data-retention failures, and soft errors. This work aims at designing low-power and robust circuits and systems by drawing circuit, architecture, and algorithm approaches. Throughout the work, new simulation methodologies are developed for fast and accurate analysis and evaluation. This dissertation explores the low-voltage limits of sequential logics. The minimum supply voltage (Vmin) for sequential logic circuits is analyzed by statistically simulating the impact of within-die process variations and gate-dielectric soft breakdown on data retention and hold time. As Vcc scales, statistical circuit simulations demonstrate that hold time increases faster than circuit delay or cycle time, consequently the required number of minimum-delay buffers increases. For this reason, we formulate a new hold-time violation metric that defines Vmin as the Vcc in which the hold time exceeds a target percentage of the cycle time. The hold-time Vmin metric is adopted by Intel as a new design metric. Device scaling increases soft errors, affecting circuit reliability. Two 65nm bulk CMOS test chips were characterized in heavy-ion radiation environment in Texas A&M University K500 superconducting cyclotron facility in 2012. Through extensive soft error characterization, we observe the soft error mechanisms and its dependence
Learning analytics: a firm basis for the future

Learning analytics take the data we leave behind us as we engage with education and use those data to improve teaching and learning. Today, those data are mainly gathered from virtual learning environments. In the future they are likely to include personal and environmental data collected by sensors that we wear or that are embedded in the objects around us. This chapter draws on two international studies to examine the possibilities offered by learning analytics, and the challenges they offer. It provides a set of key questions to ask when developing, implementing or making use of analytics.
Assessing Library Space for Learning

services, and the writing is clear and effective. One concern I have with the structure of the book is that the chapter on types of games follows chapters on setting goals and objectives, project design, and identifying partners and stakeholders. This chapter has some inspiring examples, and getting these examples up front could create more enthusiasm than the more pragmatic chapters. As librarians working with Millenials, we would be wise to consider gamification since, as Prensky (2011) noted, games have become a natural way for them to relate to information and stimuli.
Multiple Access Channels with Cooperating Encoders

The work treats the Compound Multiple Access Channel with common message and with conferencing encoders, the Arbitrarily Varying Channel with conferencing encoders, and the Wiretap Multiple Access Channel with common message and with conferencing encoders. Coding theorems are proved for the Compound Multiple Access Channel and the Arbitrarily Varying Multiple Access Channel, achievable regions are derived for the Wiretap Multiple-Access Channel.
Advanced Applications Of Big Data Analytics

Human life is progressing with advancements in technology such as laptops, smart phones, high speed communication networks etc., which helps us by reducing load in doing our daily activities. For instance, one can chat, talk, make video calls with his/her friends instantly using social networking platforms such as Facebook, Twitter, Google+, WhatsApp etc. LinkedIn, Indeed, etc., connects employees with potential employers. The number of people using these applications are increasing day-by-day, and so is the amount of data generated from these applications. Processing such vast amounts of data, may require new techniques for gaining valuable insights. Network theory concepts form the core of such techniques that are designed to uncover valuable insights from large social network datasets. Many interesting problems such as ranking top-K nodes and top-K communities that can effectively diffuse any given message into the network, restaurant recommendations, friendship recommendations on social networking websites, etc., can be addressed by using the concepts of network centrality. Network centrality measures such as In-degree centrality, Out-degree centrality, Eigenvector centrality, Katz Broadcast centrality, Katz Receive centrality, and PageRank centrality etc., comes handy in solving these problems. In this thesis, we propose different formulae for computing the strength for identifying top-K nodes and communities that can spread viral marketing messages into the network. The strength formulae are based on Katz Broadcast centrality, Resolvent matrix measure and Personalized PageRank measure. Moreover, the effects of intercommunity and intracommunity connectivity in ranking top-K communities are studied. Top-K nodes for spreading any message effectively into the network are determined by using Katz Broadcast centrality measure. Results obtained through this technique are compared with the top-K nodes obtained by using Degree centrality measure. We also studied the effects of varying α on the number of nodes in search space. In Algorithms 2 and 3, top-K communities are obtained by using Resolvent matrix and Personalized PageRank measure. Algorithm 2 results were studied by varying the parameter α.
Multimodal Personality Recognition from Audiovisual Data

Automatic behavior analysis lies at the intersection of different social and technical research domains. The interdisciplinarity of the field, provides researchers with the means to study the manifestations of human constructs, such as personality. A branch of human behavior analyis, the study of personality provides insight into the cognitive and psychological construction of the human being. Research in personality psychology, advances in computing power and the development of algorithms, have made it possible to analyze existing data in order to understand how people express their own personality, perceive others’, and what are the variables that influence its manifestation. We are pursuing this line of research because insights into the personality of the user can have an impact on how we interact with technology. Incorporating research on personality recogniton, both from a cognitive as well as an engineering perspective, into computers could facilitate the interactions between humans and machines. Previous attempts on personality recognition have focused on a variety of different corporas (ranging from text to audiovisual data), different scenarios (interviews, meetings), different channels of communication (audio, video, text) and different subsets of personality traits (out of the five ones present in the Big Five Model: Extraversion, Agreeableness, Conscientiousness, Emotional Stability and Creativity). Our work builds on previous research, by considering simple acoustic and visual non-verbal features extracted from multimodal data, but doesn’t fail to bring novelties: we consider previously uninvestigated scenarios, and at the same time, all of the five personality traits and not just a subset.
Course Allocation System for Lecturers

On this project, the purpose of system developing is to ease and smoothen the course allocation process. Moreover, a lot of issues have popped up regarding the manually allocation method such as lecturers could not get the expertise courses to be lectured, more courses given to higher position lecturers and also burden their research timing. According, to the problem the developer has come up with a solution which to develop a course allocation system for lecturers. The developer has set the objectives to provide the lecturers their expertise field course to be lectured and also provide the correct number of courses according to the lecturer position and timing. Regarding, this the developer has done a research study on the similarities of the system which has been developed previously. So that, the developer can make a comparison with the previous system and the system about to be developed. Next, the developer has chosen the Rational Unified Process (RUP) methodology to develop the system because the methodology is the backbone of the system. Moreover, the developer also has done a few data collection using the data collection method. In addition, there are few respondents who have replied and provide the vital information of the user requirement. Next,
Assessment methods for secondary wireless networks

Today’s networked society has an unprecedented and growing demand for wireless capacity. New technological innovations both in the physical layer and on the network level are required in order to enable future wireless communication systems to scale up accordingly. However, these developments will necessarily need to be aligned with improved means to manage radio spectrum, the scarce essential resource, and limit, for wireless communications. Apart from extensions to the traditional set of tools for spectrum management, new methods for granting spectrum access on a secondary use basis have been proposed recently. They have been widely discussed in the academic, industrial, and regulatory communities, albeit with only a limited number of practical implementations so far. A presumed reason for this observed hesitance are uncertainties on the technical and economical value of operating in shared spectrum. In order to fill this gap, the main contribution of this thesis is the development of a framework that enables the unified assessment of entire secondary network deployments. This framework has been furthermore implemented as a spectrum assessment tool software that allows for a detailed integrated technology and regulatory analysis. It is a primary concern in secondary access scenarios to protect the incumbent spectrum user against a deterioration of spectrum quality due to additional interference. In this thesis, we evaluate the options and necessary tradeoffs for different prototype protection models in terms of primary service quality reduction and secondary technical operation limits. We specifically quantify the primary-secondary relationship using the German TV broadcasting network as a case study for detailed analysis. Apart from this sensitivity analysis, our work provides realistic availability figures for the underutilized TV bands of Germany, commonly known as the TV whitespaces (TVWS). Current secondary access policies that are based on geo-location databases may result in excessive interference or low system performance due to the limited precision of the propagation models that they employ. This motivates us to propose the addition of functionalities to these databases that enable them to exploit auxiliary primary signal strength information to improve their prediction performance. We analyze different model-based and model-free estimation methods for their feasibility in such hybrid setups. Our analysis is based on large-scale data sets from real measurement campaigns and synthetic spectrum data. We furthermore compare our results against the theoretical bounds of the methods.
Towards a Game-Chatbot: Extending the Interaction in Serious Games

Van Rosmalen, P., Eikelboom, J., Bloemers, E. , Van Winzum, K. , & Spronck, P. (2012). Towards a Game-Chatbot: Extending the Interaction in Serious Games. In P. Felicia (Ed.), Proceedings of the 6th European Conference on Games Based Learning (pp. 525-532). October, 4-5, 2012, Cork, Ireland. Academic Publishing International Limited, Reading, UK.
TOSCA-based orchestration of complex clusters at the IaaS level

This paper describes the adoption and extension of the TOSCA standard by the INDIGO-DataCloud project for the definition and deployment of complex computing clusters together with the required support in both OpenStack and OpenNebula, carried out in close collaboration with industry partners such as IBM. Two examples of these clusters are described in this paper, the definition of an elastic computing cluster to support the Galaxy bioinformatics application where the nodes are dynamically added and removed from the cluster to adapt to the workload, and the definition of an scalable Apache Mesos cluster for the execution of batch jobs and support for long-running services. The coupling of TOSCA with Ansible Roles to perform automated installation has resulted in the definition of high-level, deterministic templates to provision complex computing clusters across different Cloud sites.
Algoritmo de cifrado para sistemas móviles

Los modernos algoritmos criptológicos responden a principios y filosofías diferentes a las que se llevaban a cabo antaño. Uno de los principios de diseño es que el algoritmo debe demostrar su resistencia a los ataques conocidos. Para ello deben contemplarse instancias o funciones, desde la mismísima etapa de diseño que demuestren su capacidad de resistir tal o cual ataque. Así demostrar la robustez y resistencia del algoritmo frente a un conjunto conocido de ataques.
Heuristic Based Power Consumption Minimisation and Optimisation on Smartphones

In the direction of capable save the energy of battery life in Smartphone accurate with sensible influence form of Wi-Fi data communication in infrastructure mode. The network environment parameters like still time in a 3G system the network throughput and the convey pattern have been accepted all through measurement study the collective impact of an issue has not been systematical.
‘But this isn’t school’: exploring tensions in the intersection between school and leisure activities in classroom game design

ABSTRACT While there are several positive outcomes from implementing game design in a formal learning context, there are also challenges that have to be considered in order to improve game-based learning. This is explored in the article, using the concepts of activity frames and stancetaking, focusing on the social organization of the game design activity. Building on video data from one 6th grade class and one 7th grade class designing computer games based on their social studies curriculum, this article shows that tensions arise when students fail to agree on what the activity they are doing is really about: The academic content and what students commonly perceive as school activities, or a game design activity informed by their leisure time. The main argument is that the students position themselves as students, game designers or characters, and that this may cause tensions in the students’ social interactions.
LSIS-DME: An Environment for Modeling and Simulation of DEVS Specifications

The paper presents a software tool to model and simulate DEVS models. The tool can be used for education of DEVS formalism or developing complex models of real systems. It is based on two main features: 1) a graphical environment in which DEVS models are specified and 2) a module that generates programming code from graphical DEVS specifications. The DEVS simulator uses the automatic generated code to make simulations.