Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of the bandwidth knowledge of performance in the acquisition of a judo motor skill

Background & Study Aim: The literature on motor learning characterizes feedback as a critical component of learning, making it an important factor in the process of motor skills acquisition. The aim of the present study was to verify the effects of the bandwidth in the acquisition of a judo motor skill. Material & Methods: Thirty-six volunteers were randomly assigned to three experimental groups: control group (CG) that received knowledge of performance (KP) in all trials; that received (KP) in a wide bandwidth (WBG); that received KP in a narrow bandwidth (NBG). The task used in this experiment was the o soto gari throw. The experiment consisted of an entrance test, acquisition phase, post-test and retention test. Results: The athletes from CG presented worse results when compared to the other bandwidths groups. Also, the wide bandwidth helped in the changes of performance and pattern of the movement, favouring the improvement of the consistency and precision. The wide bandwidth of KP aided in learning complex motor skills, and that the narrow bandwidth, despite helping to change the pattern of execution, was not enough to result in changes in performance. The wide bandwidth of KP was more adequate for sports learning. Conclusion: Taken together these findings showed that the combination of a lower frequency of KP supply, the effect of qualitative information on performance, coupled with an optimal intertrial interval, was the key to the effects of the bandwidth of KP on the acquisition of a complex task with specific demands like the judo throw: o soto gari.
Efficient Visual SLAM for Autonomous Aerial Vehicles

The general interest in autonomous or semi-autonomous micro aerial vehicles (MAVs) is strongly increasing. There are already several commercial applications for autonomous micro aerial vehicles and many more being investigated by both research institutes and multiple financially strong companies. Most commercially available applications, however, are rather limited in their autonomy: They rely either on a human operator or reliable reception of global positioning system (GPS) signals for navigation. Truly autonomous micro aerial vehicles that can also fly in GPS-denied environments such as indoors, in forests, or in urban scenarios, where the GPS signal may be blocked by tall buildings, clearly require more on-board sensing and computation potential. In this dissertation, we explore autonomous micro aerial vehicles that rely on a so-called RGBD camera as their main sensor for simultaneous localization and mapping (SLAM). Several aspects of efficient visual SLAM with RGBD cameras aimed at micro aerial vehicles are studied in detail within this dissertation: We first propose a novel principle of integrating depth measurements within visual SLAM systems by combining both 2D image position and depth measurements. We modify a widely-used visual odometry system accordingly, such that it can serve as a robust and accurate odometry system for RGBD cameras. Based on this principle we go on and implement a full RGBD SLAM system that can close loops and perform global pose graph optimization and runs in real-time on the computationally constrained onboard computer of our MAV. We investigate the feasibility of explicitly detecting loops using depth images as opposed to intensity images with a state of the art hierarchical bag of words (BoW) approach using depth image features. Since an MAV flying indoors can often see a clearly distinguishable ground plane, we develop a novel efficient and accurate ground plane detection method and show how to use this to suppress drift in height and attitude. Finally, we create a full SLAM system combining the earlier ideas that enables our MAV to fly autonomously in previously unknown environments while creating a map of its surroundings.
Neural Networks for Electron Identification with DAMPE

The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector and cosmic ray observatory in operation since 2015, equipped with alongside other instruments a deep calorimeter able to detect electrons up to an energy of 10 TeV and cosmic hadrons up to 100 TeV. The large proton and ion background in orbit requires a powerful electron identification method. In recent years, the field of machine learning has provided such tools. We explore here a neural network based approach to an on-orbit particle identification problem. We present the issues that arise from the constraints of particle physics, notably the difference between training set based on simulated data, and the application set based on real unlabeled data, leading to a trade-off between performances and general usability. We finally compare the neural network discrimination power with the more traditional cut-based analysis.
A Multiword Unit Analysis : COCA Multiword Unit List 20 and ColloGram

Multiword units receive attention as being an essential part of vocabulary knowledge that will expedite the learning of an L2. However, there is lack of a graded multiword units (MWU) list that can offer direct applications for pedagogy, syllabus design or materials development. The current article aims to report on the development and evaluation of COCA (Corpus of Contemporary American English) Multiword Unit 20 (COCA_MWU20) where the notion of MWU family is adopted. The compilation involved selecting and grading the MWU by grammatical well-formedness, range, and frequency. The 10,000 MWU families which made up the list were utilized in the development of ColloGram, a list-based MWU family analysis program. The COCA_MWU20 can be expected to help L2 learners increase their knowledge of lexical items beyond single words and ColloGram is the first of the kind that can analyze multiword items based on COCA. For researchers, the ColloGram can also be a tool for identifying MWU that may appear in a target text.
Improving the classification of urban tree diversity from Very High Spatial Resolution hyperspectral images: comparison of multiples techniques

The aim of the study is to compare and assess the efficiency of conventional hyperspectral techniques (dimension reduction, learning and classification methods) to classify the urban tree vegetation diversity. A specific focus is made using very high spatial resolution hyperspectral images acquired from airborne sensor and simulated at various spatial/spectral resolutions. Results show that 78.8% of overall accuracy can be reached using MNF and SVM methods to classify urban tree diversity. Moreover, results are more sensitive to learning methods rather than dimension reduction or classification ones. Finally, VHRS hyperspectral imagery are promising for such a key urban environmental issue.
Design and Interoperability Analysis of Quadruple Pad Structure for Electric Vehicle Wireless Charging Application

The power pad coil design is one of the most significant traits for wireless charging of electric vehicle charging applications. The proper design of the power pad is the most critical phase of developing an efficient and reliable wireless power transfer system. Each available coil design has its advantages suitable for specific applications. In this paper, the two objective problems of optimization are discussed involving optimized design, shape, size, and optimized current directions in subsquare structures of quadruple power pad structures. The dimensional design optimized to least area with maximum coupling coefficient, and current directions with least interference between corresponding coils were verified through finite-element analysis (FEA) using ANSYS Maxwell. The results were thoroughly compared among all the cases discussed. The other aspect of this paper is the compatibility analysis of quadruple coil structure with the rectangular, double D (DD), and double D quadrature (DDQ) coil structures. Results have been compared, and the quadruple coil structure is verified as compatible with all other coil structures.
Robotic models of obstacle avoidance in bats

Echolocating bats can avoid obstacles in complete darkness relying on their sonar system. Under experimental conditions, these animals can infer the 3D position of obstacles. However, in cluttered and complex environments their ability to locate obstacles is likely to be largely reduced, and they might need to rely on more robust cues that do not degrade as the complexity of the environment increases. Here, we present a robotic model of two hypothesized obstacle avoidance strategies in bats, both of which model observed behavior in bats: a Gaze Scanning Strategy and a Fixed Head Strategy. Critically, these strategies only employ interaural level differences and do not require locating obstacles. We found that both strategies were successful at avoiding obstacles in cluttered environments. However, the Fixed Head Strategy performed better. This indicates that acoustic gaze scanning, observed in hunting bats, might reduce obstacle avoidance performance. We conclude that strategies based on gaze scanning should be avoided when little or no spatial information is available to the bat, which corresponds to recent observations in bats.
“Near-Perfect” Finite-Cardinality Generalized Space-Time Shift Keying

Two decades of full-diversity high-rate MIMO research has created perfect Space-Time Block Codes (STBCs), including the Golden code. However, the major stumbling block of their wide-spread employment is their limited energy-efficiency. On one hand, the superposition of their signals results in a high Peak-to-Average Power Ratio (PAPR). On the other hand, the total number of equivalent Inter-Antenna Interference (IAI) contributions that the receiver has to deal with is increased to <inline-formula> <tex-math notation="LaTeX">$\text {IAI}=M^{2}$ </tex-math></inline-formula> upon using <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> Transmit Antennas (TAs), which is a substantial extra price compared to the <inline-formula> <tex-math notation="LaTeX">$\text {IAI}=M$ </tex-math></inline-formula> of V-BLAST. Against this background, we propose a new family of Finite-Cardinality Generalized Space-Time Shift Keying (FC-GSTSK). More explicitly, the proposed FC-GSTSK is capable of outperforming both V-BLAST and STBC, which is the ultimate objective of full-diversity high-rate MIMO design. Furthermore, following the index modulation philosophy, the proposed FC-GSTSK replaces the signal-additions by the data-carrying signal-selection process. As a benefit, the FC-GSTSK substantially reduces the PAPR of signal transmission. As a further advantage, the equivalent IAI imposed on signal detection is reduced back to the same level as that of the V-BLAST. Moreover, the proposed FC-GSTSK is even capable of consistently outperforming the perfect STBCs in terms of its Peak Signal to Noise-power Ratio (PSNR) that takes into account the power consumption at the transmitter. As a further advance, the reduced-RF-chain based version of FC-GSTSK is also capable of outperforming both Generalized Spatial Modulation (GSM) and Space-Time Block Coded Spatial Modulation (STBC-SM) without increasing the PAPR and the equivalent IAI.
Optimal Transmit Strategies for Gaussian MISO Wiretap Channels

This paper studies the optimal tradeoff between secrecy and non-secrecy rates of the MISO wiretap channels for different power constraint settings: sum power constraint only, per-antenna power constraints only, and joint sum and per-antenna power constraints. The problem is motivated by the fact that channel capacity and secrecy capacity are generally achieved by different transmit strategies. First, a necessary and sufficient condition to ensure a positive secrecy capacity is shown. The optimal tradeoff between secrecy rate and transmission rate is characterized by a weighted rate sum maximization problem. Since this problem is not necessarily convex, equivalent problem formulations are introduced to derive the optimal transmit strategies. Under sum power constraint only, a closed-form solution is provided. Under per-antenna power constraints, necessary conditions to find the optimal power allocation are derived. Sufficient conditions are provided for the special case of two transmit antennas. For the special case of aligned channels, the optimal transmit strategies can deduced from an equivalent point-to-point channel problem. Last, the theoretical results are illustrated by numerical simulations.
How does overconfidence affect information security investment and information security performance?

ABSTRACT Although organizational information security investment has attracted a great deal of attention from academia and industry, there is a lack of studies on the decision maker's overconfidence. This paper examines the relationship between overconfidence of executives, information security investment and information security performance. The study shows that overconfidence is negatively associated with information security investment and an inverted U-shaped curvilinear relationship existed between information security investment and information security performance. Furthermore, to illustrate the robustness of our results, the suppressing effect and the serial mediating role between overconfidence, information security investment and information security performance are tested finally.
Feed-forwards meet recurrent networks in vehicle trajectory prediction

For an autonomous car, getting surprised is the worst thing that can happen. To prevent that, plenty of studies are trying to forecast traffic participants’ actions especially in an urban scene using recurrent networks. Recurrent networks are used for temporal tasks in many application domains like Natural Language processing and computer vision. Despite the tendency in the literature to use recurrent neural networks for trajectory prediction, we argue that because of small dependency in trajectory sequences of a vehicle, a feed-forward neural network can be used, instead. In this paper, we will compare these two methods in vehicle trajectory prediction while considering the vanilla models or taking the scene into account. In order to have more variations in trajectories, roundabouts are used as a case study. Our results show that the proposed feed-forward network has competitive results with a recurrent network with 6 times faster processing time.
Efficient Localization of Human Actions and Moments in Videos

Efficient Localization of Human Actions and Moments in Videos Victor A. Escorcia Castillo We are stumbling across a video tsunami flooding our communication channels. The ubiquity of digital cameras and social networks has increased the amount of visual media content generated and shared by people, in particular videos. Cisco reports that 82% of the internet traffic would be in the form of videos by 2022. The computer vision community has embraced this challenge by offering the first building blocks to translate the visual data in segmented video clips into semantic tags. However, users usually require to go beyond tagging at the video level. For example, someone may want to retrieve important moments such as the “first steps of her child” from a large collection of untrimmed videos; or retrieving all the instances of a home-run from an unsegmented video of baseball. In the face of this data deluge, it becomes crucial to develop efficient and scalable algorithms that can intelligently localize semantic visual content in untrimmed videos. In this work, I address three different challenges on the localization of actions in videos. First, I develop deep-based action proposals and detection models that take a video and generate action-agnostic and class-specific temporal segments, respectively. These models retrieve temporal locations with high accuracy in an efficient manner, faster than real-time. Second, I propose the new task to retrieve and localize temporal moments from a collection of videos given a natural language query. To tackle this challenge, I introduce an efficient and effective model that aligns the text query to individual clips of fixed length while still retrieves moments spanning multiple clips. This approach not only allows smooth interactions with users via natural language 5 queries but also reduce the index size and search time for retrieving the moments. Lastly, I introduce the concept of actor-supervision that exploits the inherent compositionality of actions, in terms of transformations of actors, to achieve spatiotemporal localization of actions without the need of action box annotations. By designing efficient models to scan a single video in real-time; retrieve and localizing moments of interest from multiple videos; and an effective strategy to localize actions without resorting in action box annotations, this thesis provides insights that put us closer to the goal of general video understanding.
Artificial Intelligence, Artists, and Art

This study examines how people perceive artwork created by artificial intelligence (AI) and how presumed knowledge of an artist's identity (Human vs. AI) affects individuals’ evaluation of art. Drawing on Schema theory and theory of Computers Are Social Actors (CASA), this study used a survey-experiment that controlled for the identity of the artist (AI vs. Human) and presented participants with two types of artworks (AI-created vs. Human-created). After seeing images of six artworks created by either AI or human artists, participants (n = 288) were asked to evaluate the artistic value using a validated scale commonly employed among art professionals. The study found that human-created artworks and AI-created artworks were not judged to be equivalent in their artistic value. Additionally, knowing that a piece of art was created by AI did not, in general, influence participants’ evaluation of art pieces’ artistic value. However, having a schema that AI cannot make art significantly influenced evaluation. Implications of the findings for application and theory are discussed.
Has Digital Changed Approaches to Media Strategy Making

Purpose – This research explores concepts and processes within strategy making in general, and by media planning professionals in particular. The field of strategy making is dominated by the theory of strategic choice and characterised by an objectivist ontology. However, many believe this does not reflect strategy formulation in today’s complex and turbulent environment (Stacey & Mowles, 2015; Volberda, 2004). Design methodology – The research focused on digital media strategy and planning, an area suffering a high degree of turbulence and complexity, to identify if alternative strategy making processes are being used. The author developed a conceptual approach based on a comprehensive review of strategy making literature alongside primary research amongst UK senior Media Directors Findings – This research identifies a more emergent and iterative process than is described through the literature. This resonates with the Learning School and differs markedly from the more prescriptive Planning School approach that have previously dominated the industry. Practical implications – Data driven media planning is now more inductive, with the final plan emerging from multiple closely related iterations and optimisation. This approach is differentiated from short termism and tactics by identifying longer term, hierarchically linked business and marketing goals to guide the media planning.
Secure Precoding Aided Spatial Modulation via Transmit Antenna Selection

In this paper, two transmit antenna selection schemes are proposed for precoding-aided spatial modulation (PSM) to enhance the secrecy performance with reduced hardware complexity. The first strategy is determined by the channel state information (CSI) of both the legitimate and illegitimate links, called deterministic antenna selection based PSM (DAS-PSM), which selects partial transmit antennas to implement zero-forcing (ZF) precoding for maximizing the secrecy rate. For practical implementation, the low-complexity DAS-PSM criterion is demonstrated. The second strategy resorts to random antenna selection (RAS) with the aid of fast radio-frequency switching. Different from the DAS-PSM, this so-called RAS based PSM (RAS-PSM) dispenses with the CSI of the wiretap channel, which generates the ZF precoding matrix associated with randomly selected transmit antenna combination during each symbol period, creating additional turbulence that solely affects the eavesdropper. To combat the issue of low secrecy rate in the high signal-to-noise ratio regime due to the finite-alphabet inputs, time-varying artificial noise with extra power can be incorporated into both aforementioned strategies, which is exemplified in the RAS-PSM. Finally, the secrecy rates of both proposed schemes are theoretically analyzed and evaluated via Monte Carlo simulations, demonstrating that both proposed schemes with different requirements are capable of enhancing the secrecy performance compared to the conventional PSM.
Essay on Contract Structure in Principal-Agent Problems with Behavioral Models

Title of Dissertation: ESSAY ON CONTRACT STRUCTURE IN PRINCIPAL-AGENT PROBLEMS WITH BEHAVIORAL BIASES Hong Lin Doctor of Philosophy, 2019 Dissertation directed by: Professor Emel Filiz-Ozbay Department of Economics Designing employment contracts in a principal-agent relationship is a key problem in the modern firm theory. This dissertation investigates this problem from three different angles, (1) the employment contracts in the labor market with procrastinating workers; (2) the behavior of members and reciprocal leaders in group competitions, where leaders can reward members discretionarily; (3) optimal employment contracts when tasks are endogenously designed. For the chapter about the employment contracts as a commitment device, I build an adverse selection model in a labor market of one firm against many workers, where the workers, if self-employed, procrastinate due to their own quasihyperbolic discounting. In the equilibrium, the model shows that workers with the least procrastination are self-employed and workers with the most procrastination are part-time employees in a separating equilibrium where the workers’ hiring contracts differ by their quasi-hyperbolic discounting. In between, there exist specific ranges of quasi-hyperbolic discounting factors, in each of which the workers sign the same contract in a pooling equilibrium. This model leads to a position hierarchy within the firm as well as separation of paid-employment and self-employment in the labor market. For the chapter about the behavior of reciprocal leaders and members in group competitions, I model the model equilibrium when the leaders are reciprocal and show the existence of the pure strategy equilibrium. A laboratory group all-pay auctions was run to test for the model predictions. For the chapter about the optimal employment contracts with endogenous tasks, I examine the optimal job design in a principal-agent setting where tasks are designed by the principal. The principal wants to incentivize agents to exert a given desired amount of effort on an effort space; to minimize the cost of reaching this goal, the principal can freely assign disjoint parts of the effort space to agents as their jobs and partition each job into tasks. I provide a characterization of the optimal number of agents to be hired and optimal partition of each agent’s job into tasks. Essay on Contract Structure in Principal-Agent Problems with Behavioral Models