Miracle-dz/UniFAR-FTUs huggingface dataset:
Contact-sensing Input Device Manipulation and Recall

We study a cuboid tangible pen-like input device similar to Vogel and Casiez’s Conté. A conductive 3D-printed Conté device enables touch sensing on a capacitive display, and orientation data from an enclosed inertial measurement unit (IMU) reliably distinguishes all 26 corners, edges, and sides. The device’s size is constrained by hardware required for sensing. We evaluate the impact of size form-factor on manipulation times for contact-to-contact transitions. A controlled experiment logs manipulation times performed with three sizes of 3D printed mock-ups of the device. Computer vision techniques reliably distinguish between all 26 possible contacts, and a resistive touch sensor provides accurate timing information. In addition, a transition to touch input is tested, and a mock-up of a digital pen is included as a baseline comparison. Results show larger devices are faster, contact-to-contact transition time increases with distance between contacts, but transitions to barrel edges can be slower than some end-over-end transitions. A comparison with a pen-shaped baseline indicates no loss in transition speed for most equivalent transitions. Based on our results, we discuss ideal device sizes and improvements to the simple extruded-rectangle form-factor. Subsequently, we evaluate learning and recall of commands located on physical landmarks on the exterior of a 3D tangible input device in comparison with a 2D spatial interface. Each of the 26 contacts is a physical spatial landmark on the exterior of Conté. A pilot study compares command learning and recall for Conté with a 2D grid interface, using small and large commands sets. To facilitate novice learning, an on-screen model of Conté replicates the physical device’s orientation and displays icons representing commands on the corresponding landmarks. Results show there is likely no difference between 2D and 3D spatial interface recall for a small command set and high recall is possible with large command sets. Applications illustrating possible use cases are discussed as well as possible improvements to the on-screen guide based on our results.
Cartoon Effect and Ambient Illumination Based Depth Perception Assessment of 3D Video

Monitored 3-Dimensional (3D) video experience can be utilized as “feedback information” to fine tune the service parameters for providing a better service to the demanding 3D service customers. The 3D video experience which includes both video quality and depth perception is influenced by several contextual and content related factors (e.g., ambient illumination condition, content characteristics, etc) due to the complex nature of the 3D video. Therefore, effective factors on this experience should be utilized while assessing it. In this paper, structural information of the depth map sequences of the 3D video is considered as content related factor effective on the depth perception assessment. Cartoon-like filter is utilized to abstract the significant depth levels in the depth map sequences to determine the structural information. Moreover, subjective experiments are conducted using 3D videos associated with cartoon-like depth map sequences to investigate the effectiveness of ambient illumination condition, which is a contextual factor, on depth perception. Using the knowledge gained through this study, 3D video experience metrics can be developed to deliver better service to the 3D video service users. Keywords—3D Video, Ambient Illumination, Cartoon Effect, Depth Perception.
Who Selects an Online Class over the Same Course Face-to-Face?

Recommended Citation Parcel, Toby L. ; Radu, Monica Bixby; and Gonzales, Laura Fitzwater (2018) "Who Selects an Online Class Over the Same Course Face-to-Face? And Who Learns More? Results from a Mixed-Methods, QuasiExperimental Study of Teaching the Sociology of Work and Family," The Journal of Public and Professional Sociology: Vol. 10 : Iss. 1 , Article 6. Available at: https://digitalcommons.kennesaw. edu/jpps/vol10/iss1/6
An Exodus II specification for handling gauss points.

This report specifies the way in which Gauss points shall be named and ordered when storing them in an EXODUS II file so that they may be properly interpreted by visualization tools. This naming convention covers hexahedra and tetrahedra. Future revisions of this document will cover quadrilaterals, triangles, and shell elements.
Teaching a Powered Prosthetic Arm with an Intact Arm Using Reinforcement Learning

The idea of an amputee playing the piano with all the flair and grace of an ablehanded person may seem like a futuristic fantasy. While many prosthetic limbs look lifelike, finding one that also moves naturally has proved more of a challenge for both researchers and amputees. Even though sophisticated upper extremity prostheses like the Modular Prosthetic Limb (MPL) are capable of effecting almost all of the movements as a human arm and hand, they can be useful only if robust systems of control are available. The fundamental issue is that there is a significant mismatch between the number of controllable functions available in modern prosthetic arms and the number of control signals that can be provided by an amputee at any given moment. In order to bridge the gap in control, we require a neural interface that can translate the physiological signals of the user into a large number of joint commands for simultaneous, coordinated control of the artificial limb. In this thesis, we focus on a collaborative approach towards the control of powered prostheses. In our approach, the user shifts greater autonomy to the prosthetic device, thereby sharing the burden of control between the human and the machine. In essence, the prosthesis or rehabilitative device learns to “fill in the gaps” for the user. With this view in our mind, we developed a method that could allow someone with an amputation to use their non-amputated arm to teach their prosthetic arm how to move in a natural and coordinated way by simply showing the prosthetic arm the right way to move in response to inputs from the user. Such a paradigm could well exploit the muscle synergies already learned by the user. Consider cases where an amputee has a desired movement goal, e.g., “add sugar to my coffee,” “button up my shirt,” or “shake hands with an acquaintance”. In these more complicated examples, it may be difficult for a user to frame their objectives in terms
Protection of Multiterminal HVDC Grids based on Modular Multilevel Converters: DC Fault Ride-Through and AC Grid Support

High Voltage DC (HVDC) grids provide an efficient solution for the transmission of bulk power over long distances between the energy production and consumption centers. Up to now, most of the implemented dc connections are point-to-point, creating dc links to strengthen the existing, predominantly ac, electricity grid. However, to optimize the use of assets and energy harvest, multi-terminal HVdc grids are envisioned. Based on recent research and industrial trends, the Multilevel Modular Converter (MMC) technology will be the building block for the realization of these grids. Yet, as the grid structure becomes more complex, the protection of HVDC grids poses one of the most important challenges. This dissertation proposes different ways to isolate and ride through dc faults, while maintaining controllability of the converters that can offer ancillary services to their respective ac grid, having as a main objective to reduce the downtime of the grid and the time in which assets are not utilised. More specifically: The characteristics of dc faults and the main parameters which affect the fault response are investigated. A dc breaker optimized design is proposed which allows equipment sharing, offering bidirectional isolation capability for multiple lines at the same time. In case MMC with fault-blocking capability is used, e.g. Full-bridge MMC, a methodology for dc fault ride-through is proposed. Moreover, a dc current controller is presented, ensuring that the MMC can continue its controlled operation towards the respective ac grid. Once the dc side is isolated, the MMC operation as STATCOM is studied. More specifically, a control structure is proposed to maintain internal balancing of the converter, while ensuring Low-Voltage-Ride-Through (LVRT). The capability of the MMC to operate as Active Power Filter (APF) is also investigated. A selective harmonics detection and control method is presented and experimentally veri_ed for the mitigation of high-order current and voltage harmonics up to 13th order. Each of the aforementioned topics is dealt with in a respective Chapter of this dissertation.
METHOD OF SEGMENTATION OF MEDICAL IMAGES

Рассматриваются математические методы сегментации изображений стандарта Dicom. Разрабатываются математические методы сегментации изображений стандарта Dicom длязадач распознавания медицинских изображений. Диагностика заболеваний зависит от квалификации исследователя и требует от него визуально проводить сегментацию, а математические методы по обработке растровых изображений являются инструмен-том для данной диагностики. Обработка полученных аппаратным обеспечением медицинских изображений без предварительнойткиобрабо графических данных в большинстве случаев дает неверные результаты. Вы-полнялись процедуры выделения контуров объектов методом Canny и дополнительными алгоритмами обра-ботки растровых изображений. Результаты исследований позволяют вычислить необходимые для дальнейшего лечения пациента морфометрические, геометрические и гистограммные свойства образований в организме человека и обеспечить эффективное медицинское лечение. Разработанные принципы компьютерного автома-тизированного анализа медицинских изображений эффективно используются для оперативных задач медицин-ской диагностики специализированного онкологического учреждения, так и в учебных целях. Ключевые слова
System Security And Monitoring On Smart Home Using Android

Home security system is needed for homeowners who have a lot of activities, as a result, they often leave the house without locking the door and even leave the house in a state of lights that are not lit. In order to overcome this case, a system that can control and can monitor the state of the various devices contained in the house or smart home system is urgently required. The working principle of this smart home using android is when the homeowner sends a certain command using android, the command will be forwarded to the microcontroller and then it will be executed based on the parameters that have been determined. For example, it can turn off and on the light using android app. In this study, testing was conducted to a smart home prototype which is equipped with light bulbs, odour sensors, heat sensors, ultrasonic sensors, LDR, buzzer and camera. The test results indicate that the application has been able to control all the sensors of home appliances well.
Stereo Imaging for 3D Scene Reconstruction

Stereo Vision plays an important role in computer and robot vision. It is similar in concept to human binocular vision useful for three dimensional scene reconstruction. This research presents a novel stereo vision technique with a fast left-right searching for stereo matching. The system has been furnished with the disparity estimation of the image sequences using the sum of absolute differences (SAD) and sum of squared differences (SSD) methods and reconstruction of the 3D scene from the obtained disparity map. This research presents three major contributions: (i) the implementation of a fuzzy filtering to reduce additive noises from the stereo images, (ii) the development of a fast stereo correspondence estimation method called “left-right searching” that increases matching accuracy and reduces computation time substantially with no appreciable degradation of accuracy, and (iii) the development of a new stereo based model for 3D scene reconstruction. Experimental results indicate that the proposed stereo vision system is capable of matching correct pixels more than 70% and it reduces the window cost computation time of about 44% compared to other traditional techniques.
Tracking radar with LPI characteristics based on high rate SS-FH

A new signal modulation format used for a tracking radar showing very low probability of detection and interception is proposed. The radar working principle is based on the application of spread spectrum-frequency hopping technique. The surveillance is done radiating a very low power microwave signal during very short pulses spread randomly over a large bandwidth dramatically reducing the radiated energy. A simulation SW has been developed to study the system performance under different conditions, and a system demonstrator has been built using low to medium cost component technology.
Exploring Sampling Methodology for Obtaining Reliable Text Coverage

Increasingly, “text coverage” is being used in studies to measure the intelligibility of word lists and second language learning material, and because, to date, there are no established standards for calculating text coverage, the reliability of resulting data and its practical application to language learning is called into question. This study addresses this issue by applying empirical analyses (distribution of mean score and standard deviation) to text coverage samples using variations of text length (both with and without proper nouns), vocabulary size and sample size in order to determine how these variables might affect the calculation of text coverage. Twenty-six different text lengths taken from CNN news transcripts and 22 lists of graded vocabulary range taken from high frequency words of the British National Corpus were analyzed using 10 different sample sizes in 1,000 iterations. The results of the study clearly demonstrate that text coverage is more stable when the text length is longer, when more samples are used, and when proper nouns are excluded. When proper nouns were retained, the coverage figures were 10-12% less than when they were excluded. As a practical guideline for educators, a table showing minimum parameters is included for reference in computing text coverage calculations.
Frontiers, challenges, and opportunities for information retrieval: Report from SWIRL 2012 the second strategic workshop on information retrieval in Lorne

During a three-day workshop in February 2012, 45 Information Retrieval researchers met to discuss long-range challenges and opportunities within the field. The result of the workshop is a diverse set of research directions, project ideas, and challenge areas. This report describes the workshop format, provides summaries of broad themes that emerged, includes brief descriptions of all the ideas, and provides detailed discussion of six proposals that were voted "most interesting" by the participants. Key themes include the need to: move beyond ranked lists of documents to support richer dialog and presentation, represent the context of search and searchers, provide richer support for information seeking, enable retrieval of a wide range of structured and unstructured content, and develop new evaluation methodologies.
A Customized Real Time Restaurant Recommendation System

According to a Search Engine Land survey conducted in 2014, 88% of consumers have read online reviews to determine the quality of a local business and 72% consumers say that positive reviews make them trust a local business more. What is a local business? It can be a medical practice, a restaurant, or a gym. The result of this survey shows the important role of reviews in deciding to choose a local business. Among various types of business, restaurant is the most searched category by users in Yelp. However, oftentimes people just search a restaurant by using word “restaurant”, while the word “restaurant” means differently to different individuals. For an Asian, it can mean a “Chinese restaurant” or “Thai restaurant”. How to correctly interpret search requests based on people’s preference is a challenge. Building a machine-learning model based on activity history of a registered user can solve this problem. The activity histories used by this thesis are reviews and ratings from users. This thesis introduces a data processing pipeline, which uses reviews from registered users to generate a machine-learning model for each business and each registered user. This thesis also defines an architecture, which uses the generated machine-learning models to support real-time personalized recommendations for restaurant searching.
An Advisor System for Cultural Adaptation in Instructional Design

In an age of globalization, it is important to consider cultural variables in the instructional design process, but as [2], [3], [7] and [16] point out, too few tools and guidelines exist to assist instructional designers in this task. Also, as pointed out by [13], professors are not always aware or even informed of the existence of cultural variables in pedagogical practices. We used a Design-based Research (DBR) iterative approach to identify cultural variables in the instructional design professional culture and we modeled knowledge regarding these variables via a formal ontology on the basis of which we created a “Cultural Diversity” knowledge base. Our “Cultural Diversity” knowledge base brings together knowledge regarding five cultures. Our advisor system, through an executable assistance process for cultural adaptation, uses this knowledge to advise the instructional designer, who then proceeds to adapt a pedagogical scenario to a culture other than his or her own. In this article, we present the executable assistance process model which enables the staging of different software agents that advise the designer, who must carry out the cultural adaptation of his or her pedagogical scenario.
Design of Restoration Method Based on Compressed Sensing and TwIST Algorithm

In order to improve the subjective and objective quality of degraded images at low sampling rates effectively,save storage space and reduce computational complexity at the same time, this paper proposes a joint restoration algorithm of compressed sensing and two step iterative threshold shrinkage (TwIST). The algorithm applies the TwIST algorithm which used in image restoration to the compressed sensing theory. Then, a small amount of sparse high-frequency information is obtained in frequency domain. The TwIST algorithm based on compressed sensing theory is used to accurately reconstruct the high frequency image. The experimental results show that the proposed algorithm achieves better subjective visual effects and objective quality of degraded images while accurately restoring degraded images.
An extensible visual environment for construction and analysis of hierarchically-structured models of resource contention systems

The development of models for evaluating the performance of resource contention systems, such as manufacturing systems, computer systems, and communication networks, is often a difficult and complex task. This modeling effort can be dramatically reduced by the use of appropriate software tools. The Research Queueing Package Modeling Environment (RESQME) provides a graphical environment for constructing, solving, and analyzing the results of extended queueing network models of resource contention systems. It supports a rich underlying modeling paradigm previously developed in the Research Queueing Package (RESQ) and provides a single integrated graphical interface throughout all tasks of the modeling lifecycle. In this paper we present a brief overview of RESQME and then focus on two of its most important features: the construction and analysis of hierarchically-structured models and the ability to extend and customize the RESQME environment for domain-specific modeling via the use of user-defined modeling elements. A manufacturing model is developed in order to illustrate these capabilities.
Control and estimation problems in mobile communication systems

Introduces two problems in cellular communication systems related to control and estimation. The first problem deals with distributed power control. It is shown that the cooperative algorithm which is a distributed, iterative algorithm to handle the static power control problem exhibits desirable convergence properties even for certain dynamic situations. The second problem deals with the tracking of mobile users by a novel registration scheme. The idea is based on the concept of a Bloom filter.
No Right Answer: A Feasibility Study of Essay Assessment with LDA

Essay scoring is a difficult and contentious business. The problem is exacerbated when there are no “right” answers for the essay prompts. This research developed a simple toolset for essay analysis by integrating a freely available Latent Dirichlet Allocation (LDA) implementation into a homegrown assessment assistant. The complexity of the essay assessment problem is demonstrated and illustrated with a representative collection of open-ended essays. This research also explores the use of “expert vectors” or “keyword essays” for maximizing the utility of LDA with small corpora. While, by itself, LDA appears insufficient for adequately scoring essays, it is quite capable of classifying responses to open-ended essay prompts and providing insight into the responses. This research also reports some trends that might be useful in scoring essays once more data is available. Some observations are made about these insights and a discussion of the use of LDA in qualitative assessment results in proposals that may assist other researchers in developing more complete essay assessment software.
A Video-Oculography Based Telemedicine System for Automated Nystagmus Identification

This paper presents the research and results for an automated jerk-type nystagmus identification system that makes use of an efficient, low cost video-oculography (VOG) device, designed for telemedicine applications. The pupil position is estimated by a hybrid tracking algorithm from the captured VOG images. It is also shown that wavelet analysis with an appropriate mother wavelet, coupled with well-defined geometric constraints can provide a reliable and robust nystagmus identification algorithm. Some original research regarding robust analysis for signals with mixed content is also presented.
Stochastic systems divergence through reinforcement learning

Modelling real-life systems and phenomena using mathematical based formalisms is ubiquitous in science and engineering. The reason is that mathematics oﬀer a suitable framework to carry out formal and rigorous analysis of these systems. For instance, in software engineering, formal methods are among the most eﬃcient tools to identify ﬂaws in software. The behavior of many real-life systems is inherently stochastic which require stochastic models such as labelled Markov processes (LMPs), Markov decision processes (MDPs), predictive state representations (PSRs), etc. This thesis is about quantifying the diﬀerence between stochastic systems. The important point of the thesis is that reinforcement learning (RL), a branch of artiﬁcial intelligence particularly eﬃcient in presence of uncertainty, can be used to quantify eﬃciently the divergence between stochastic systems. The key idea is to deﬁne an MDP out of the systems to be compared and then to interpret the optimal value of the MDP as the divergence between them. The most appealing feature of the proposed approach is that it does not rely on the knowledge of the internal structure of the systems.