Miracle-dz/UniFAR-FTUs huggingface dataset:
A Toolkit to Assist Researchers to More Efficiently Conduct Experiments Assessing Human State

For teams to perform effectively, individuals must focus on their own tasks, while simultaneously maintaining awareness of other team members. Researchers studying and attempting to optimize performance of teams as well as individual team members use assessments of behavioral, neurophysiological, and physiological signals that correlate with individual and team performance. However, synchronizing data from multiple sensor devices can be difficult, and building and using models to assess human states of interest can be time-consuming and non-intuitive. To assist researchers, we built an Adaptable Toolkit for the Assessment and Augmentation of Performance by Teams in Real Time (ADAPTER), which provides a framework that flexibly integrates sensors and fuses sensor data to assess performance. ADAPTER flexibly integrates current and emerging sensors; assists researchers in creating and implementing models that support research on performance and the development of augmentation strategies; and enables comprehensive and holistic characterization of team member performance during real-time experimental protocols.
Design synthesis & prototype implementation of parallel orientation manipulators for optomechatronic applications

This thesis documents a research endeavor undertaken to develop high-performing designs for parallel orientation manipulators (POM) capable of delivering the speed and the accuracy requirements of a typical optomechatronic application. In the course of the research, the state of the art was reviewed, and the areas in the existing design methodologies that can be potentially improved were identified, which included actuator design, dimensional synthesis of POMs, control system design, and kinematic calibration. The gaps in the current art of designing each of these POM system components were addressed individually. The outcomes of the corresponding development activities include a novel design of a highly integrated voice coil actuator (VCA) possessing the speed, the size, and the accuracy requirements of small-scale parallel robotics. Furthermore, a method for synthesizing the geometric dimensions of a POM was developed by adopting response surface methodology (RSM) as the optimization tool. It was also experimentally shown how conveniently RSM can be utilized to develop an empirical quantification of the actual kinematic structure of a POM prototype. In addition, a motion controller was formulated by adopting the active disturbance rejection control (ADRC) technology. The classic formulation of the ADRC algorithm was modified to develop a resource-optimized implementation on control hardware based on field programmable gate arrays (FPGA). The practicality and the effectiveness of the synthesized designs were ultimately demonstrated by performance benchmarking experiments conducted on POM prototypes constructed from these components. In specific terms, it was experimentally shown that the moving platforms of the prototyped manipulators can achieve highspeed motions that can exceed 2000 degrees/s in angular velocity, and 5×105 degrees/s2 in angular acceleration.
Fine‐Grained Mobile Application Clustering Model Using Retrofitted Document Embedding

In this paper, we propose a fine‐grained mobile application clustering model using retrofitted document embedding. To automatically determine the clusters and their numbers with no predefined categories, the proposed model initializes the clusters based on title keywords and then merges similar clusters. For improved clustering performance, the proposed model distinguishes between an accurate clustering step with titles and an expansive clustering step with descriptions. During the accurate clustering step, an automatically tagged set is constructed as a result. This set is utilized to learn a high‐performance document vector. During the expansive clustering step, more applications are then classified using this document vector. Experimental results showed that the purity of the proposed model increased by 0.19, and the entropy decreased by 1.18, compared with the K‐means algorithm. In addition, the mean average precision improved by more than 0.09 in a comparison with a support vector machine classifier.
Query Cluster: A Method for Web Search Behavior

— There have been intensive research on user web search behavior since the late 1990s. Previous researchers collected data from search engines and analyzed explicit data (queries) to understand the characteristics of the user’s search process, while other researchers analyzed the data of recruited subjects under experimental settings to understand the behavioral patterns in web usage. Although these researches provided an understanding of what users are searching for and how they are searching, both approaches did not provide rich user contexts that capture the reason why users are motivated to search, how long users’ tasks (session) last, and other factors affecting user’s search behavior. In this paper, we propose ‘Clustered Query’ as the unit of analysis in web search behavior studies. We found that users make their own Clustered-queries that yield better overview on their web search pattern, yet detailed individual web traces intact. The methodology consists of three phases and Log Catcher , Query Cluster , Monitoring tool , and Retrospective Interview technique are used in each phase. At the end of this paper, we also illustrate the process of the pilot and main study where the methodology is modified and validated.
Real-Time Obstacle Avoidance for Continuum Manipulator: Towards Safer Application in Human Environments

The rigid-link manipulators have been extensively employed in industrial setting nowadays. However, problem arises when such manipulators are assigned to work alongside human or employed in cluttered, complex, and tight environment. Hence, continuum manipulator, with its inherent compliant and flexibility, is preferable in this aspect. In this paper, we present our recent work presenting a real-time pose estimation and obstacle avoidance for continuum manipulator in dynamic environments. The approach is shown to work in a model of both single-segment and multi-segment continuum manipulator and also in a real tendon-driven single-segment continuum manipulator in dynamic environment, and thus, suitable to be used in human environments.
Model-based camera tracking for augmented reality

MODEL-BASED CAMERA TRACKING FOR AUGMENTED REALITY Aytek Aman M.S. in Computer Engineering Supervisor: Prof. Dr. Uğur Güdükbay August, 2014 Augmented reality (AR) is the enhancement of real scenes with virtual entities. It is used to enhance user experience and interaction in various ways. Educational applications, architectural visualizations, military training scenarios and pure entertainment-based applications are often enhanced by augmented reality to provide more immersive and interactive experience for the users. With hand-held devices getting more powerful and cheap, such applications are becoming very popular. To provide natural AR experiences, extrinsic camera parameters (position and rotation) must be calculated in an accurate, robust and efficient way so that virtual entities can be overlaid onto the real environments correctly. Estimating extrinsic camera parameters in real-time is a challenging task. In most camera tracking frameworks, visual tracking serve as the main method for estimating the camera pose. In visual tracking systems, keypoint and edge features are often used for pose estimation. For rich-textured environments, keypoint-based methods work quite well and heavily used. Edge-based tracking, on the other hand, is more preferable when the environment is rich in geometry but has little or no visible texture. Pose estimation for edge based tracking systems generally depends on the control points that are assigned on the model edges. For accurate tracking, visibility of these control points must be determined in a correct manner. Control point visibility determination is computationally expensive process. We propose a method to reduce computational cost of the edge-based tracking by preprocessing the visibility information of the control points. For that purpose, we use persistent control points which are generated in the world space during preprocessing step. Additionally, we use more accurate adaptive projection algorithm iii
A new approach on 3D scanning-printing technologies with medical applications

The 3D printer is a very much discussed subject nowadays. It has many positive aspects and its purpose is to minimize costs, streamline complex processes, and shorten implementation times. From simple applications to complex systems, almost any assembly or subassembly can be reproduced using 3D printers, either based on a design or on a 3D real-world scan. Medicine is one of the domains that has turned to these devices, achieving amazing performance. 3D scanning and printing are basic elements in the development of new processes that were not possible at the moment. Thus, there are 3D models of the external ear of the human ear, applications in the repair of the epithelium affected by certain external factors, prostheses, medical models, and heart valves. In the present paper, we aim to explore, presenting advantages and disadvantages, existing systems for 3D scanning and designing of 3D models for printing in the field of orthopaedics.
Network representation based on the joint learning of three feature views

Network representation learning plays an important role in the field of network data mining. By embedding network structures and other features into the representation vector space of low dimensions, network representation learning algorithms can provide high-quality feature input for subsequent tasks, such as network link prediction, network vertex classification, and network visualization. The existing network representation learning algorithms can be trained based on the structural features, vertex texts, vertex tags, community information, etc. However, there exists a lack of algorithm of using the future evolution results of the networks to guide the network representation learning. Therefore, this paper aims at modeling the future network evolution results of the networks based on the link prediction algorithm, introducing the future link probabilities between vertices without edges into the network representation learning tasks. In order to make the network representation vectors contain more feature factors, the text features of the vertices are also embedded into the network representation vectors. Based on the above two optimization approaches, we propose a novel network representation learning algorithm, Network Representation learning algorithm based on the joint optimization of Three Features (TFNR). Based on Inductive Matrix Completion (IMC), TFNR algorithm introduces the future probabilities between vertices without edges and text features into the procedure of modeling network structures, which can avoid the problem of the network structure sparse. Experimental results show that the proposed TFNR algorithm performs well in network vertex classification and visualization tasks on three real citation network datasets.
Analyzing and enhancing music mood classification : an empirical study

In the computer age, managing large data repositories is one of the common challenges, especially for music data. Categorizing, manipulating, and refining music tracks are among the most complex tasks in Music Information Retrieval (MIR). Classification is one of the core functions in MIR, which classifies music data from different perspectives, from genre to instrument to mood. The primary focus of this study is on music mood classification. Mood is a subjective phenomenon in MIR, which involves different considerations, such as psychology, musicology, culture, and social behavior. One of the most significant prerequisitions in music mood classification is answering these questions: what combination of acoustic features helps us to improve the accuracy of classification in this area? What type of classifiers is appropriate in music mood classification? How can we increase the accuracy of music mood classification using several classifiers? To find the answers to these questions, we empirically explored different acoustic features and classification schemes on the mood classification in music data. Also, we found the two approaches to use several classifiers simultaneously to classify music tracks using mood labels automatically. These methods contain two voting procedures; namely, Plurality Voting and Borda Count. These approaches are categorized into ensemble techniques, which combine a group of classifiers to reach better accuracy. The proposed ensemble methods are implemented and verified through empirical experiments. The results of the experiments have shown that these proposed approaches could improve the accuracy of music mood classification.
Predicting How to Test Requirements: An Automated Approach

An important task in requirements engineering is to identify and determine how to verify a requirement (e.g., by manual review, testing, or simulation; also called potential verification method). This information is required to effectively create test cases and verification plans for requirements. [Objective] In this paper, we propose an automatic approach to classify natural language requirements with respect to their potential verification methods (PVM). [Method] Our approach uses a convolutional neural network architecture to implement a multiclass and multilabel classifier that assigns probabilities to a predefined set of six possible verification methods, which we derived from an industrial guideline. Additionally, we implemented a backtracing approach to analyze and visualize the reasons for the network’s decisions. [Results] In a 10-fold cross validation on a set of about 27,000 industrial requirements, our approach achieved a macro averaged F1 score of 0.79 across all labels. For the classification into test or non-test, the approach achieves an even higher F1 score of 0.94. [Conclusions] The results show that our approach might help to increase the quality of requirements specifications with respect to the PVM attribute and guide engineers in effectively deriving test cases and verification plans.
Adaptive fuzzy dynamic surface tracking control for a class of nonlinear systems with unknown distributed time delays and backlash-like hysteresis

In this article, an adaptive fuzzy backstepping dynamic surface control approach is developed for a class of nonlinear systems with unknown backlash-like hysteresis and unknown state discrete and distributed time-varying delays. Fuzzy logic systems are used to approximate the unknown nonlinear functions and a fuzzy state observer is designed for estimating the immeasurable states. Then, by combining the backstepping technique and the appropriate Lyapunov–Krasovskii functionals with the dynamic surface control approach, the output-feedback adaptive fuzzy tracking controller is designed. The main advantages of this article are (i) the existence of the state discrete and distributed time-varying delays such that the investigated systems are more general than that of the existing results, (ii) the proposed control scheme can eliminate the problem of “explosion of complexity” inherent in the backstepping design method, and (iii) for the nth nonlinear system, only one fuzzy logic system is used to approximate the unknown continuous time-varying delay functions since all of them are lumped into one unknown nonlinear function, which makes our design scheme easier to be implemented in practical applications. It is proven that the proposed design method is able to guarantee that all the signals in the closed-loop system are bounded and the tracking error can converge to a small neighborhood of origin with an appropriate choice of design parameters. Finally, the simulation results demonstrate the effectiveness of the proposed approach.
Semantic coercion: Rethinking motion verbs in Thinking-For-Speaking

The categorization of motion verbs in THINKING-FOR-SPEAKING (TfS) research paradigm entails some theoretical problems that need to be addressed. This conclusion is motivated by the examination of motion verb types listed in Berman &Slobin (1994, p. 198) for Spanish and English narrators of ‘the frog story’ (Mayer, 1969). These lists show a range of semantically different verb types including verbs describing MANNER VIA MOTION (e.g., climb), CAUSE VIA MOTION (e.g., push), PATH VIA MOTION (e.g., come), SOUND-EMISSION (e.g., buzz), and ACTION (e.g., knock). We argue that in the absence of any clear theoretical framework for why, how and whether these verbs should be categorized as motion verbs, TfS research runs the risk of slipping into inconsistency. To this end, interdisciplinary insights about the behavior of motion verbs in English suggest that SEMANTIC COERCION – the process whereby non-motion verbs are coerced into the expression of motion meanings – merits more attention in TfS research than it currently receives. The notion of MOTIONIZATIONis offered as a first step in this direction.
Wireless receiving system and method with automatic gain control

The invention relates to a wireless receiving system with automatic gain control, which comprises a receiving path, an analog-to-digital conversion device, an automatic gain control device and a control device, wherein the control device is provided with an adjacent frequency interference closing model, an adjacent frequency interference acquisition model and an adjacent frequency interference tracking model; the automatic gain control device is further set respectively according to the adjacent frequency interference closing model, the adjacent frequency interference acquisition model and the adjacent frequency interference tracking model, thereby the gain of a plurality of moulds in the receiving path is adjusted, in other words, the gain is adjusted at the right time aiming at the strength of different adjacent frequency interferences, and the optimal communication receiving quality is achieved.
AN EVALUATION OF THE PERFORMANCE OF LEGGINGS BASED ON A CONSUMER SURVEY

OF THESIS AN EVALUATION OF THE PERFORMANCE OF LEGGINGS BASED ON A CONSUMER SURVEY The purpose of this research was to identify the legging features that athleisure consumers desire and the performance problems they frequently encounter. A nonprobability sample (n=133) of college students who identified as female and wore leggings for non-athletic purposes were surveyed. The questionnaire was designed based upon examination of online product reviews and current complaints with legging products. The results of the survey were used to design and complete a laboratory evaluation. Laboratory samples and methods were then chosen to assess the performance qualities desired when purchasing leggings and the problems encountered when wearing leggings by the surveyed consumers. Three brands were identified by the survey as regularly consumed legging brands and included: Lululemon, Nike, and 90 Degree. All laboratory samples were a nylon, spandex blend fabric. Garments were laundered and evaluated after one, five, ten, and twenty washes. All evaluations were completed in a Textile Testing Laboratory according to AATCC and ASTM standard test methods.
Band selection for hyperspectral image classification using extreme learning machine

Extreme learning machine (ELM) is a feedforward neural network with one hidden layer, which is similar to a multilayer perceptron (MLP). To reduce the complexity in the training process of MLP using the traditional backpropagation algorithm, the weights in ELM between input and hidden layers are random variables. The output layer in the ELM is linear, as in a radial basis function neural network (RBFNN), so the output weights can be easily estimated with a least squares solution. It has been demonstrated in our previous work that the computational cost of ELM is much lower than the standard support vector machine (SVM), and a kernel version of ELM can offer comparable performance as SVM. In our previous work, we also investigate the impact of the number of hidden neurons to the performance of ELM. Basically, more hidden neurons are needed if the number of training samples and data dimensionality are large, which results in a very large matrix inversion problem. To avoid handling such a large matrix, we propose to conduct band selection to reduce data dimensionality (i.e., the number of input neurons), thereby reducing network complexity. Experimental results show that ELM using selected bands can yield similar or even better classification accuracy than using all the original bands.
Assessing the performance of two non-commercial bare earth classification algorithms for LiDAR data sets within the southern Appalachians

CLASSIFICATION ALGORITHMS FOR LIDAR DATA SETS WITHIN THE SOUTHERN APPALACHIANS by ANDREW PARKER (Under the Direction of Thomas Jordan) ABSTRACT Two non-commercial LiDAR bare earth classification algorithms were assessed for their suitability to accurately filter ground points from non-ground points in a forested mountainous area within the southern Appalachians. These algorithms were the multiscale curvature classification algorithm (MCC) and the adaptive triangular irregular network algorithm (ATIN). The filtered ground points were used to create three meter resolution experimental surfaces that were compared to three meter resolution control surfaces created from commercial LiDAR filtering software. Suitability for use as an alternative to commercial filtering algorithms was determined for the MCC and ATIN algorithms. The areas of the experimental surfaces that represented elevation deviation from that of the control surfaces were examined for the likelihood that the landscape characteristics of the study are were influencing vertical error sustained during classification implementation.
Removing spam from web corpora through supervised learning using FastText

Unlike traditional text corpora collected from trustworthy sources, the content of web based corpora has to be filtered. This study briefly discusses the impact of web spam on corpus usability and emphasizes the importance of removing computer generated text from web corpora. The paper also presents a keyword comparison of an unfiltered corpus with the same collection of texts cleaned by a supervised classifier trained using FastText. The classifier was able to recognize 71% of web spam documents similar to the training set but lacked both precision and recall when applied to short texts from another data set.