Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing Redundancy: A Practice Perspective on Emergent Standardization in a Hospital Unit

Data redundancy constitutes a challenge in modern healthcare systems, and is therefore critical to address. This has nurtured a sustained debate on how this challenge may be addressed through standardization of e.g. information models and technologies. However, redundant data also has an important role in clinical practice. This calls for improved understanding on the role of standards, and flexibility that enables necessary redundancy. In this paper, we present findings from an interpretative case study conducted at an emergency care unit in a Norwegian hospital. We followed health professionals in all phases of the patient trajectories at the unit. We focused on how redundancy was produced and used in connection with new standardized data representations in newly implemented HISs. The paper aims to answer the following research questions: What are the implications of a local implementation of a standardized data representation for data redundancy? How do these dynamics condition emergent standardization?
A Comparative Study of Polarity Lexicons to Identify Extreme Opinions

This paper comparing a method to automatically build a sentiment lexicon, with four well-known sentiment lexicons. For this purpose, an indirect evaluation is carried out. The lexicons are integrated into supervised sentiment classifiers and their performance is evaluated in two sentiment classification tasks in order to identify i) the most negative vs. not most negative opinions, and ii) the most positive vs. not most positive. Moreover, a set of textual features is integrated into the classifiers so as to analyze how these textual features improve the lexicon performance.
Analysis of Energy Efficiency for Clustering-based Wireless LAN in Free Space and Multipath Environments

Energy consumption is a major concern in the communication of wireless LAN (WLAN) because the WLAN requires high-energy resources. One way to reduce energy consumption is to use clustering on the network. Clustering is useful in network scalability and energy savings. A WLAN typically experience symptoms called multipath that may take the form of reflection, diffraction, and free space. This paper analyses energy efficiency for WLAN based clustering in free space and multipath environments. The research method has several stages consisting of a system model, mathematical analysis and computer simulation for clustering-based WLAN in both environments. The simulation results show that the bit rate and data size have the impact on energy consumption and energy efficiency on the network. The higher bit rate, the lower the energy consumption in the networks. The energy consumption increases as the data size increases. Then, high-energy efficiency can be achieved by using the higher bit rate in the networks. Moreover, the energy efficiency of clustering-based WLAN in free space is higher than that of the multipath environment. It means that multipath transmission has the impact on the energy consumption and efficiency.
Redundancy-Free UAV Sensor Fault Isolation And Recovery

The sensory system of unmanned aerial vehicles (UAVs) plays an important role in flight safety. Thus, any sensor fault/failure can have potentially catastrophic effects on vehicle control. The recent advance in adversarial studies demonstrated successful sensing fault generation by targeting the physical vulnerabilities of the sensors. It poses new security challenges for sensor fault detection and isolation (FDI) and fault recovery (FR) research because the conventional redundancy-based fault-tolerant design is not effective against such faults. To address these challenges, we present a redundancy-free method for UAV sensor FDI and FR. In the FDI design, we used a basic state estimator for a rough early warning of faults. We then refine the design by considering the unmeasurable actuator state and modeling uncertainties. Under such novel strategies, the proposed method achieves fine-grained fault isolation. Based on this method, we further designed a redundancy-free FR method by using complementary sensor estimations. In particular, position and attitude feedback can provide backup feedback for each other through geometric correlation. The effectiveness of our approach is validated through simulation of several challenging sensor failure scenarios. The recovery performance is experimentally demonstrated by a challenging flight tasks-restoring control after completely losing attitude sensory feedback. With the protection of FDI and FR, flight safety is ensured. This UAV security enhancement method is promising to be generalized for other types of vehicles and can serve as a compensation to other fault-tolerant methodologies.
Non-uniform Illumination Video Enhancement Based on Zone System and Fusion

In daily life, the acquisition of digital video will introduce non-uniform exposure due to the set of shooting equipment or scene illumination. Due to the low visibility, details are hidden and these videos usually fail to present visually pleasing browsing. Previous work typically relies on a single heuristic tone mapping curve to expand the dynamic range, which inevitably leads to uneven exposure. To solve this problem, we present a new video enhancement method based on zone system and image fusion. Given an input non-uniform illumination video, we first apply the zone system for exposure evaluation. We then remap each region using a series of tone mapping curves to generate multi-exposure regions which contain different exposed versions. Guided by some visual perception quality measures, we locate all the best exposed regions and then integrate them into a well-exposed video frame. Finally, in order to keep temporal consistency, we temporally propagate the zone regions from the former frame to the current frame. Experimental results have shown that the enhanced video exhibits uniform exposure, and preserves temporal consistency.
The Implementation of Wash, Rinse, and Spin Technique in Accelerometer’s Data Processing on Android Smartphone to Generate Stream Keys

The needs of random key generator on Android smartphones grow in line with applications which provide cryptographic services. Overcoming this we can use sensor on Android itself to be a key generator, that called Accelerometer sensor. However, raw data generated from this sensor are weak data sources because they have many slow drifts. According to those background, Hong & Liu proposed the protocol that consists of three data-processing techniques, it Wash, Rinse, and Spin. This study implements the protocol by using the accelerometer sensor in android smartphone. It uses SDLC method to build an application. Differentiate process used in Wash step, FFT Radix-2 and SecureRandom() in Rinse step then Pseudo Random Bit Generator (PRBG) based on Chaotic Logistic Map used in the Spin step. Some tests such as Unit, Performance, and Usability testing are conducted to evaluate the application. The uit testing result shows the protocol is properly applied to Android-based random key generator application. The performance testing result show it needs 8.2 milliseconds to generate a 128-bit key. It graphically transforms and shuffles data on vary key’s lengths and device condition. It has 4.8 milliseconds average time to save the generated keys into the smartphone’s memory.
Software Architecture for Sensor Nodes in the Internet of Things: A Case of Study in Agriculture

This paper presents a software architecture for microcontrollers running on data capture cards for the Internet of Things (IoT). The Software Architecture describes the structure, operation and interaction of the different components from a global perspective. This paper describes various components and subcomponents of the architecture and its communication. A case study is developed based on the proposed architecture for a cilantro crop in Colombia.
Stability Verification for Periodic Trajectories of Autonomous Kite Power Systems

State-feedback controllers are frequently employed in Airborne Wind Energy systems to follow reference flight paths. In this paper we compute the region of attraction (ROA) of path-stabilizing feedback gains around a periodic trajectory as typically flown by a power generating kite. The feedback gains are obtained from applying a time-varying periodic Linear Quadratic Regulator to the system expressed in transversal coordinates. To compute the ROA we formulate Lyapunov stability conditions which we verify by Sum-of-Squares programs. These programs are posed as an optimization problem for which we compare various definitions of the cost function representing the size of the ROA. In a numeric case study we demonstrate that the maximization of an expanding ellipse inside of quartic Lyapunov functions leads to significantly larger verified ROAs compared to the standard approach of optimizing over the scaling of the sublevel set of a quadratic Lyapunov function.
Mitigating Mirai Malware Spreading in IoT Environment

Internet of Things(IoT) allows humans and everyday physical objects to interact and facilitates information sharing. Limited computational & memory capabilities, heterogeneous nature and lack of security mechanisms in IoT give rise to security and privacy concerns. Hence, vulnerable IoT devices lure attackers to exploit and turn them into a bot. A Botnet is a collection of these bots used to perform DDoS attacks later. Many IoT security breaches have been reported recently, which have disrupted the access to popular websites. In this paper, we analyzed the most recent Mirai malware, its exploitation techniques and proposed whitelisting based solution to prevent IoT Botnet from spreading and our experiments illustrates the successful blocking of Mirai malware
Does Premium Subscription Pay Off? Evidence from Online Dating Platform

While freemium business model is gaining increasing popularity, we are still unclear about how the IT-enabled premium features affect users’ behavior. We study this question in the context of an online dating platform. Drawing support from the framework of purchase funnel, we decompose individuals’ online dating behavior into three stages, including consideration, evaluation, and matching. We construct a daily panel dataset consisting of users’ premium subscription information, micro-level behavioral data, and demographic information. Using propensity score matching in combination with difference-in-differences estimator, we evaluate the causal impact of premium subscription on users’ behavior during the whole dating process. We find that premium adoption leads to an increase in outcomes at each stage. Namely, after subscribing premium, users visit more profiles, approach more people through private messages, and achieve more matches. However, premium features do not appear to enhance the efficiency of finding a match. This study extends the literature on freemium business model and online dating by investigating how the IT-enabled targeted search and information collection influence people’s online dating behavior.
Task-Driven Dictionary Learning based on Convolutional Neural Network Features

Modeling data as a linear combination of a few elements from a learned dictionary has been used extensively in the recent decade in many fields, such as machine learning and signal processing. The learning of the dictionary is usually performed in an unsupervised manner, which is most suitable for regression tasks. However, for other purposes, e.g. image classification, it is advantageous to learn a dictionary from the data in a supervised way. Such an approach has been referred to as task-driven dictionary learning. In this work, we integrate this approach with deep learning. We modify this strategy such that the dictionary is learned for features obtained by a convolutional neural network (CNN). The parameters of the CNN are learned simultaneously with the task-driven dictionary and with the classifier parameters.
Feeling Presence in Immersive Virtual Reality for Individual Learning

Virtual Reality (VR) technologies have been on the rise since Oculus Rift entered the market in 2016. These head-mounted displays offer great potential to change individual learning through certain characteristics. They provide an enclosed virtual environment that allows interactions with virtual objects. Users can become immersed in this environment, i.e., totally engaged in a specific task while ignoring other attentional demands. While using VR for individual learning, these characteristics have not been sufficiently investigated. As a consequence, learning theories, such as cognitive absorption (CA), that deal with immersion are assumed to be essential to explain learning. To strengthen immersion, I consider presence (a sense of being there in a distant (virtual) place) to be a key predictor of CA. Therefore, I derive designs for the feeling of presence and propose and test a research model. I conducted an experiment with 63 participants and discuss some initial results for future research.
A Linear Incremental Nyström Method for Online Kernel Learning

Although the incremental Nyström method has been used in kernel approximation, it is not suitable for online kernel learning due to the cubic time complexity and the lack of theoretical guarantees. In this paper, we propose a novel incremental Nyström method, which is in a linear time complexity with respect to the sampling size at each round, and enjoys a sublinear regret bound for online kernel learning. We construct the intersection matrix using the ridge leverage score estimator, compute the rank-k approximation of the intersection matrix incrementally via the incremental singular value decomposition, and recalculate the generalized inverse matrix periodically. When applying the proposed incremental Nyström method to online kernel learning, we approximate the kernel matrix using the updated generalized inverse matrix at each round, and formulate the explicit feature mapping by the singular value decomposition of the approximated kernel matrix, yielding the linear classifier for online kernel learning at each round. Theoretically, we prove that our incremental Nyström method has a $(1+\epsilon)$ relative-error bound for kernel matrix approximation, enjoys a sublinear regret bound using online gradient descent method for online kernel learning, and reduces the time complexity of generalized inverse computation from $O(m^{3})$ to $O(mk)$ at each round, where $m$ is the sampling size and $k$ is the truncated rank. Experimental results show that the proposed incremental Nyström method is accurate and efficient in kernel matrix approximation and is suitable for online kernel learning.
Graph Similarity based on Graph Fourier Distances

Graph theory is a branch of mathematics which is gaining momentum in the signal processing community due to their ability to efficiently represent data defined on irregular domains. Quantifying the similarity between two different graphs is a crucial operation in many applications involving graphs, such as pattern recognition or social networks' analysis. This paper focuses on the graph similarity problem from the emerging graph Fourier domain, leveraging the spectral decomposition of the Laplacian matrices. In particular, we focus on the intuition that similar graphs should provide similar frequency representation for a particular graph signal. Similarly, we argue that the frequency responses of a particular graph filter applied to two similar graphs should be also similar. Supporting results based on numerical simulations support the aforementioned hypothesis and show that the proposed graph distances provide a new tool for comparing graphs in the frequency domain.
Online Low-Rank Metric Learning via Parallel Coordinate Descent Method

11The corresponding author is Prof. Yang Cong. This work is supported by Nature Science Foundation of China under Grant (61722311, U1613214, 61533015) and CAS-Youth Innovation Promotion Association Scholarship (2012163)Recently, many machine learning problems rely on a valuable tool: metric learning. However, in many applications, large-scale applications embedded in high-dimensional feature space may induce both computation and storage requirements to grow quadratically. In order to tackle these challenges, in this paper, we intend to establish a robust metric learning formulation with the expectation that online metric learning and parallel optimization can solve large-scale and high-dimensional data efficiently, respectively. Specifically, based on the matrix factorization strategy, the first step aims to learn a similarity function in the objective formulation for similarity measurement; in the second step, we derive a variational trace norm to promote low-rankness on the transformation matrix. After converting this variational regularization into its separable form, for the model optimization, we present an parallel block coordinate descent method to learn the optimal metric parameters, which can handle the high-dimensional data in an efficient way. Crucially, our method shares the efficiency and flexibility of block coordinate descent method, and it is also guaranteed to converge to the optimal solution. Finally, we evaluate our approach by analyzing scene categorization dataset with tens of thousands of dimensions, and the experimental results show the effectiveness of our proposed model.
The Block Synthesis of The Tracking System with Sigmoidal Local Feedbacks

In this paper we consider the tracking problem for a given action for a linear SISO plant, which model is presented in the input-output form. The problem is posed in the presence of noisy measurements and restrictions on the state variables and control action. The information about the derivatives of the given action is absent. The block synthesis procedure with nonlinear sigmoidal functions in feedbacks is developed for the solution of this problem. Whereas the estimation of state variables and measurement filtering are performed by using Kalman-Bucy filter.
Analysis and Comparison of High Power Bidirectional Isolated DC/DC Converters for Power Electronic Transformer

Bidirectional isolated DC/DC converter is the core component of power electronic transformer (PET). And bidirectional series resonant DC/DC converter (SRC), dual active bridge DC/DC converter (DAB) and dual bridge series resonant DC/DC converter (DBSRC) are three great candidates for PET application. The operation principles and control methods of these converters are briefly analyzed in this paper. Then, the performance in current stress, output voltage characteristic, hard-switching current and system efficiency are compared from the perspective of simulation and experiments, whose results help to select a suitable DC/DC topology for PET.
Analysis of Activity States of Local Neuronal Microcircuits in Mouse Brain

Time series of neuronal activity corresponding to different activity states in mouse brain are analyzed in the time domain and the time-frequency domain. The signals are associated with either a slow wave brain state or a persistent brain state. For both states, characteristic spectral features are identified and a simple detector is proposed that is able to identify the brain state with low latency and high accuracy. In practice, being able to monitor the brain state online and in real time is crucial for improved in vivoexperiments and, ultimately, for a causal understanding of brain dynamics.
Gender Stereotypes in a Children's Television Program: Effects on Girls' and Boys' Stereotype Endorsement, Math Performance, Motivational Dispositions, and Attitudes

Television programs are a central part of children's everyday lives. These programs often transmit stereotypes about gender roles such as “math is for boys and not for girls. ” So far, however, it is unclear whether stereotypes that are embedded in television programs affect girls' and boys' performance, motivational dispositions, or attitudes. On the basis of research on expectancy-value theory and stereotype threat, we conducted a randomized study with a total of 335 fifth-grade students to address this question. As the experimental material, we used a television program that had originally been produced for a national TV channel. The program was designed to show children that math could be interesting and fun. In the experimental condition, the program included a gender stereotyped segment in which two girls who were frustrated with math copied their math homework from a male classmate. In the control condition, participants watched an equally long, neutral summary of the first part of the video. We investigated effects on boys' and girls' stereotype endorsement, math performance, and different motivational constructs to gain insights into differential effects. On the basis of prior research, we expected negative effects of watching the stereotypes on girls' performance, motivational dispositions, and attitudes. Effects on the same outcomes for boys as well as children's stereotype endorsement were explored as open questions. We pre-registered our research predictions and analyses before conducting the experiment. Our results provide partial support for short-term effects of gender stereotypes embedded in television programs: Watching the stereotypes embedded in the video increased boys' and girls' stereotype endorsement. Boys reported a higher sense of belonging but lower utility value after watching the video with the stereotypes. Boys' other outcome variables were not affected, and there were also no effects on girl's performance, motivational dispositions, or attitudes. Results offer initial insights into how even short segments involving gender stereotypes in television shows can influence girls' and boys' stereotype endorsement and how such stereotypes may constitute one factor that contributes to gender differences in the STEM fields.
FDTD Modeling of the Landmine Detection via Ground Penetrating Radar Response

The paper deals with the simulation of the Ground Penetrating Radar (GPR) response to the Iandmines buried in a realistically simulated soil. GPR is a geophysical method that uses radar pulses to image the subsurface. However, since the detection of anti-personnel (AP) Iandmines represents a life threatening application of GPR, simulation and analysis of the response in order to apply correct interpretation in the real-life situation is of paramount importance. In this paper, gprMax open source program that simulates electromagnetic wave propagation is used. The program facilitates Finite Difference Time Domain (FDTD) method to solve Maxwell’s equations. The simulations of the Iandmine response presented in this paper serve as an opener to the subject of more comprehensive analysis oriented to demining operations and represents an effort to adequately model real life scenarios.
Explore and Learn: Optimized Two-Stage Search for Millimeter-Wave Beam Alignment

Swift and accurate alignment of transmitter (Tx) and receiver (Rx) beams is one of the fundamental design challenges to enable reliable outdoor millimeter-wave communications. In this paper, we propose a new Optimized Two-Stage Search (OTSS) algorithm for Tx-Rx beam alignment via spatial scanning. In contrast to one-shot exhaustive search, OTSS judiciously divides the training energy budget into two stages. In the first stage, OTSS explores and trains all candidate beam pairs and then discards a set of less favorable pairs learned from the received energy profile. In the second stage, OTSS takes an extra measurement for each of the survived pairs and combines with the previous measurement to determine the best one. For OTSS, we derive an upper bound on its misalignment probability, under a single-path channel model with training codebooks of ideal beam pattern. We also characterize the decay rate function of the upper bound with respect to the training budget and further derive the optimal design parameters of OTSS that maximize the decay rate. OTSS is proved to asymptotically outperform state-of-the-art baselines, and its advantage is also numerically confirmed for limited training budget and with ideal or practically synthesized beams.