Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Reduction for Interactive Geometry Processing

The research field of geometry processing is concerned with the representation, analysis, modeling, simulation and optimization of geometric data. In this thesis, we introduce novel techniques and efficient algorithms for problems in geometry processing, such as the modeling and simulation of elastic deformable objects, the design of tangential vector fields or the automatic generation of spline curves. The complexity of the geometric data determines the computation time of algorithms within these applications. The high resolution of modern meshes, for example, poses a big challenge when geometric processing tools are expected to perform at interactive rates. To this end the goal of this thesis is to introduce fast approximation techniques for problems in geometry processing. One line of research to achieve this goal will be to introduce novel model order reduction techniques to problems in geometry processing. Model order reduction is a concept to reduce the computational complexity of models in numerical simulations, energy optimizations and modeling problems. New specialized model order reduction approaches are introduced and existing techniques are applied to enhance tools within the field of geometry processing. In addition to introducing model reduction techniques, we make several other contributions to the field. We present novel discrete differential operators and higher order smoothness energies for the modeling of tangential (n-)vector fields. These are used, to develop novel tools for the modeling of fur, stroke based renderings or anisotropic reflection properties on meshes. We propose a geometric flow for curves in shape space that allows for the processing and creation of animations of elastic deformable objects. A new optimization scheme for sparsity regularized functionals is introduced and used to compute natural, localized deformations of geometrical objects. Lastly, we reformulate the classical problem of spline optimization as a sparsity regularized optimization problem.
Precise Vehicle Position and Heading Estimation Using a Binary Road Marking Map

Road markings are always present on roads to guide and control traffic. Therefore, they can be used at any time for vehicle localization. Moreover, they can be easily extracted by using light detection and ranging (LIDAR) intensity because they are brightly colored. We propose a vehicle localization method using a 2D road marking grid map. The grid map inserts the map information into the grid directly. Thus, an additional process (such as line detection) is not required and there is no problem due to false detection. We obtained road marking using a 3D LIDAR (Velodyne HDL-32E) and binarized this information to store in the map. Thus, we could reduce the map size significantly. In the previous research, the road marking grid map was used only for position estimation. However, we propose a position-and-heading estimation algorithm using the binary road marking grid map. Accordingly, we derive more precise position estimation results. Moreover, position reliability is an important factor for vehicle localization. Autonomous vehicles may cause accidents if they cannot maintain their lane momentarily. Therefore, we propose an algorithm for evaluating map matching results. Consequently, we can use only reliable matching results and increase position reliability. The experiment was conducted in Gangnam, Seoul, where GPS error occurs largely. In the experimental results, the lateral root mean square (RMS) error was 0.05 m and longitudinal RMS error was 0.08 m. Further, we obtained a position error of less than 50 cm in both lateral and longitudinal directions with a 99% confidence level.
Hand Gesture Recognition Based on Faster-RCNN Deep Learning

: The hand gesture recognition is an effective way to apply human computer interaction which should aim at good recognition accuracy and high speed. To solve the problem of hand gesture recognition in complex scenes, we propose a new approach for hand gesture recognition which is based on the faster regional convolutional neural network (Faster-RCNN) deep learning algorithm with five layers of neural network. A benchmark database with gestures is used, several general characters under the complex environmental background are chosen to as the processing object. The model is established with a certain number samples for training. The results of model checking show that the algorithm can identify the gesture recognition categories effectively, quickly and accurately with low computational cost. The accuracy can reach highly up to 99.2% which is great significance for human computer interaction application.
Eliciting Requirements for Demand Response Service Design to Households: A Pilot Study

Demand response has been discussed as one of the key means through which peak energy demand could be ameliorated. A wide variety of research and trial studies have been conducted on this topic. Yet, there has been no requirements elicitation exercise undertaken to explicitly state the requirements that a smart appliance automation service must address to be accepted by households at large. This paper presents a pilot study on just such an exercise, identifying key themes from pilot interviews. From these themes we identify several groups of requirements, such as a need to retain ultimate appliance control (e.g., through manual overrides); need for discriminating between specific appliances and their contexts for automation; and the need to be able to associate personal meaning to the service participation process by setting own goals.
How do empirical software engineering researchers assess the credibility of practitioner-generated blog posts?

Background: Blog posts offer potential benefits for research, but also present challenges. The use of blog posts in SE research is contentious for some members of the community. Also, there are no guidelines for evaluating the credibility of blog posts. Objective: To empirically investigate SE researchers' opinions on the credibility of blog posts, and identify criteria for evaluating blog posts. Method: We conduct an online survey of software engineering researchers (n=43), to gather opinions on blog-post credibility and credibility criteria. Results: There is diversity of opinion. The majority of researchers provide a qualified response to the credibility of blog posts: essentially, it depends. Several credibility criteria are valued by researchers, such as Reasoning, Clarity of writing, Reporting empirical data and Reporting methods of data collection. Approximately 60% of respondents thought the criteria generalised to other practitioner-and researcher--generated content. Conclusion: The survey constitutes the first empirical benchmark of the credibility of blog posts in SE research, and presents an initial set of criteria for evaluating the credibility of blog posts. The study would benefit from independent replication and evaluation.
A High-Performance Routing Protocol for Multimedia Applications in MANETs

—In recent years, mobile ad hoc networks (MANETs) have attracted a lot of work. One of the hottest research topics for MANETs is the routing problem that concentrates mainly on determining a good enough route from a source to a destination. Today, this means that the derived route must satisfy certain constraints or quality of service (QoS) guarantees required by multimedia applications. Therefore, the provision of QoS assurances is needed for any MANET routing protocol. Routes in MANETs are often not reliable and stable while most of multimedia applications require time-sensitive data transmission services. Thus, we propose a high-performance routing protocol to meet the QoS requirements of multimedia applications operating over MANETs. This QoS routing protocol utilizes the remaining bandwidth and transmission delay as the routing metrics for route selection. Our proposal is more flexible than existing solutions as it relies on a two-mode algorithm to identify the best-effort route according to the changes in the requirements of multimedia applications.
Acute and Post-acute Neuromodulation Induces Stroke Recovery by Promoting Survival Signaling, Neurogenesis, and Pyramidal Tract Plasticity

Repetitive transcranial magnetic stimulation (rTMS) has gained interest as a non-invasive treatment for stroke based on the data promoting its effects on functional recovery. However, the exact action mechanisms by which the rTMS exert beneficial effects in cellular and molecular aspect are largely unknown. To elucidate the effects of high- and low-frequency rTMS in the acute-ischemic brain, we examined how rTMS influences injury development, cerebral blood flow (CBF), DNA fragmentation, neuronal survival, pro- and anti-apoptotic protein activations after 30 and 90 min of focal cerebral ischemia. In addition, inflammation, angiogenesis, growth factors and axonal outgrowth related gene expressions, were analyzed. Furthermore, we have investigated the effects of rTMS on post-acute ischemic brain, particularly on spontaneous locomotor activity, perilesional tissue remodeling, axonal sprouting of corticobulbar tracts, glial scar formation and cell proliferation, in which rTMS was applied starting 3 days after the stroke onset for 28 days. In the high-frequency rTMS received animals reduced DNA fragmentation, infarct volume and improved CBF were observed, which were associated with increased Bcl-xL activity and reduced Bax, caspase-1, and caspase-3 activations. Moreover, increased angiogenesis, growth factors; and reduced inflammation and axonal sprouting related gene expressions were observed. These results correlated with reduced microglial activation, neuronal degeneration, glial scar formation and improved functional recovery, tissue remodeling, contralesional pyramidal tract plasticity and neurogenesis in the subacute rTMS treated animals. Overall, we propose that high-frequency rTMS in stroke patients can be used to promote functional recovery by inducing the endogenous repair and recovery mechanisms of the brain.
Diversification and Coarse-Grained Metaheuristics

The metaheuristics have to explore the search space successfully i.e., their search process has to explore distinctive areas of the search space and to move to unexplored parts of the search space. There are numerous factors that affect this important feature of the metaheuristics that lead to getting high quality solutions. Recently, it has been shown that the performance of the coarse-grained algorithms outperforms many other parallel algorithms. This work experimentally investigates the impact of the key parameters of the coarse-grained algorithms such as migration on the diversification (creating various solutions in order to investigate the search space on the global level) of these algorithms and subsequently their ability to find high quality solutions. The observed results show comparable recommendations.
Neural Network with Confidence Kernel for Robust Vibration Frequency Prediction

Image-based measurement has received increasing attention as it can substantially reduce the cost of labor, measurement equipment, and installation process. Instead of using optical flow, pattern, or marker tracking to extract a displacement signal, in this study, a novel noncontact machine learning-based system was proposed to directly predict vibration frequency with high accuracy and good reliability by using image sequences acquired from a single camera. The performance of the proposed method was demonstrated through experiments conducted in a laboratory and under real-field conditions and compared with those obtained using a contacted sensor. The vibration frequency prediction results of the proposed method are compared with industry-level vibration sensor results in the frequency domain, demonstrating that the proposed method could predict the target-object-vibration frequency as accurately as an industry-level vibration sensor, even under uncontrollable real-field conditions with no additional enhancement or extra signal processing techniques. However, only the principal vibration frequency of a measurement target is predicted, and the measurement range is limited by the trained model. Nonetheless, if these limitations are resolved, this method can potentially be used in real engineering applications in mechanical or civil structural health monitoring thanks to the simple deployment and concise pipeline of this method.
The Influence of Speed and Position in Dynamic Gesture Recognition for Human-Robot Interaction

Human communication relies on several aspects beyond the speech. One of them is gestures as they express intentions, interests, feelings, or ideas and complement the speech. Social robots need to interpret these messages to allow a more natural Human-Robot Interaction. In this sense, our aim is to study the effect of position and speed features in dynamic gesture recognition. We use 3D information to extract the user’s skeleton and calculate the normalized position for all of its joints, and using the temporal variation of such positions, we calculate their speeds. Our three datasets are composed of 1355 samples from 30 users. We consider 14 common gestures in HRI involving upper body movements. A set of classification techniques is evaluated testing these three datasets to find what features perform better. Results indicate that the union of both speed and position achieves the best results among the three possibilities, 0.999 of F-score. The combination that performs better to detect dynamic gestures in real time is finally integrated in our social robot with a simple HRI application to run a proof of concept test to check how the proposal behaves in a realistic scenario.
An Efficient Outlier Detection Approach Over Uncertain Data Stream Based on Frequent Itemset Mining

Outlier detection is essential in data-based science, it aims at detecting the itemsets that with a significant difference to other data. With the limiting of equipment precision and network transmission, the uncertain data is more common in daily life. However, the traditional outlier detection methods are not applicable for uncertain data stream and the large volume of data makes the outlier detecting occupy large memory usage and time cost, moreover, the multiple scanning times on data stream for Apriori-like methods are unrealistic. In this paper, the matrix structure is constructed to store the information of uncertain data stream and the following mining process is conducted with matrix structure, therefore, the whole data stream only need to be scanned for only one times. Then, the “ upper cap ” concept is used in FIM-UDS method to mine the frequent itemsets more effectively to support outlier detecting. Moreover, two outlier factors and outlier detection method that called FIM-UDSOD are designed to detect the potential outliers. Finally, two public datasets are used to verify the efficiency of FIM-UDS method and one synthetic dataset is used for evaluating FIM-UDSOD method, the experimental results show that our proposed FIM-UDSOD method is more effective in outlier detecting.
Cerebrovascular Resistance in Healthy Aging and Mild Cognitive Impairment

Measures of cerebrovascular reactivity (CVR) are used to judge the health of the brain vasculature. In this study, we report the use of several different analyses of blood oxygen dependent (BOLD) fMRI responses to CO2 to provide a number of metrics of CVR based on the sigmoidal resistance response to CO2. To assess possible differences in these metrics with age, we compiled atlases reflecting voxel-wise means and standard deviations for four different age ranges and for a group of patients with mild cognitive impairment (MCI) and compared them. Sixty-seven subjects were recruited for this study and scanned at 3T field strength. Of those, 51 healthy control volunteers between the ages of 18–83 were recruited, and 16 (MCI) subjects between the ages of 61–83 were recruited. Testing was carried out using an automated computer-controlled gas blender to induce hypercapnia in a step and ramp paradigm while monitoring end-tidal partial pressures of CO2. Surprisingly, some resistance sigmoid parameters in the oldest control group were increased compared to the youngest control group. Resistance amplitude maps showed increases in clusters within the temporal cortex, thalamus, corpus callosum and brainstem, and resistance reserve maps showed increases in clusters within the cingulate cortex, frontal gyrus, and corpus callosum. These findings suggest that some aspects of vascular reactivity in parts of the brain are initially maintained with age but then may increase in later years. We found significant reductions in all resistance sigmoid parameters (amplitude, reserve, sensitivity, midpoint, and range) when comparing MCI patients to controls. Additionally, in controls and in MCI patients, amplitude, range, reserve, and sensitivity in white matter (WM) was significantly reduced compared to gray matter (GM). WM midpoints were significantly above those of GM. Our general conclusion is that vascular regulation in terms of cerebral blood flow (CBF) responsiveness to CO2 is not significantly affected by age, but is reduced in MCI. These changes in cerebrovascular regulation demonstrate the value of resistance metrics for mapping areas of dysregulated blood flow in individuals with MCI. They may also be of value in the investigation of patients with vascular risk factors at risk for developing vascular dementia.
Self-organizing cell tactile perception which depends on mechanical stimulus history

Living organisms are capable of self-reproduction, self-repair, and high adaptability through self-organization. By applying biological materials such as cells and tissues to a robot, the resulting bio-machine hybrid system can perform important biological functions beyond those of traditional robots. In this study, we focused on the mechanobiological properties of a C2C12 cell, which is an immortalized mouse myoblast cell line. Calcium ion levels increased when the cells were subjected to mechanical stimuli. This type of physiological phenomenon can be applied in novel tactile sensors to develop a bio-machine hybrid system. We verified the tactile sensation characteristics of the cells. We cultured the cells on a thin polydimethylsulfoxane (PDMS) membrane. We applied two types of mechanical stimuli to opposite sides of the PDMS membrane. Next, we observed the cell distributions and quantified the increase in calcium ion levels inside the cells for tactile sensation. Our results demonstrate that the mechanical stimuli caused significant changes in the orientation and local density of cell aggregation. Interestingly, we confirmed that calcium ion increases depend on the history of mechanical stimuli application. GRAPHICAL ABSTRACT
Off-block Time Prediction Using Operators’ Prediction History

Target Off-Bock Time (TOBT) is one of the most uncertain factors in airport operation. TOBT is the pushback start time predicted by aircraft operators. Since TOBT is affected by uncertain factors such as passengers’ boarding process, it is difficult to predict the accurate TOBT. The author argues that TOBT accuracy is not constant and can vary depending on the situation. This TOBT accuracy information can help to improve the airport operation. Therefore, the author proposes to predict TOBT distribution, instead of predicting TOBT at a single point. TOBT is assumed to follow Johnson-SU distribution, and the parameters of the distribution are estimated by neural network. TOBT is provided and updated by aircraft operators if required, so this TOBT update history is used to predict TOBT distribution. The benefit to predict TOBT distribution is found to assign the pushback approval time (TSAT), because the probability of each aircraft reaching the runway can be calculated at any given time. The simulation result shows the significant benefit by predicting TOBT as a distribution.
Rapid Freshening of Iceland Scotland Overflow Water Driven by Entrainment of a Major Upper Ocean Salinity Anomaly

Newly available mooring observations from the Overturning in the Subpolar North Atlantic Program (OSNAP) show an abrupt decline in Iceland Scotland Overflow (ISOW) salinity from 2017 to 2018 summer. Previous declines in ISOW salinity of similar magnitude have largely been attributed to changes in convectively formed deep waters in the Nordic Seas on decadal time scales. We show that this rapid decline in salinity was driven by entrainment of a major upper ocean salinity anomaly in the Iceland Basin. This is shown by tracking the propagation of the upper ocean anomaly into ISOW using a combination of mooring and Argo observations, surface drifter trajectories, and numerical model results. A 2‐year total transit time from the upper ocean into the ISOW layer was found. The results show that entrainment allows for rapid modification of ISOW, and consequently the lower limb of Atlantic Meridional Overturning Circulation, on subdecadal timescales.
Do software engineering practitioners cite software testing research in their online articles?: A larger scale replication

Background: Software engineering (SE) research continues to study the degree to which practitioners perceive research as relevant to practice. Such studies typically comprise surveys of practitioner opinions. In a preliminary, and relatively small scale, study of online articles we previously found few explicit citations to software testing research. Our previous study provided an in situ complement to the typical survey study, however the findings of the previous study were limited by the size of our sample. Objective: To further investigate whether and how practitioners cite software testing research in the grey literature, by using a larger and more diverse dataset. Method: We analyse four distinct datasets totalling over 400,000 online articles with approx. 2M external citations. Two datasets were generated by crawling predefined domains and two were generated by applying heuristics, developed in prior research, in Google searches. Citations are classified and then analysed. Results: We find a (very) low percentage of citations to research. Conclusion: Our replication corroborates our preliminary study and findings from others. In relative terms, topic--specific searches appear to return results that contain articles with more citations. Our results and method provide a basis for benchmarking.
An Empirical Usability Analysis of the Google Authentication API

Millions of web users today use their Google accounts to sign into millions of relying party websites. This is enabled through the Google authentication API, which allows third party application developers to embed Google sign-in into their application. However, regardless to the strength of the Google authentication mechanism, the majority of these applications have been identified to be infected with broken authentication, which made them vulnerable to cyber attacks. A major reason for this is mistakes that developers make while embedding Google sign-in into their application. High complexity and lack of usability of the Google authentication API makes it difficult for programmers to use it correctly and lead them to make mistakes while using the API. In this study, we evaluated the usability of the Google authentication API by conducting a user study with 10 programmers, where they attempted to embed Google sign-in and sign-out into a web application via Google authentication API. We employed think-aloud approach to evaluate the experience of the programmers and they also provided their feedback by answering the cognitive dimension framework based questionnaire. Results of the experiment revealed 12 usability issues that exist in the Google authentication API. We discussed how these usability issues would affect the security of the application that are developed using the Google authentication API and how the API should be improved to provide a better experience to application developers.