Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of an ocular wound chamber for the prevention of exposure keratopathy in a guinea pig model

Current therapies available to treat and heal ocular surface injuries and periocular burns are frequently inadequate, costly, and labor intensive. To address these limitations, we have employed a flexible, semitransparent ocular wound chamber (OWC) to provide protection as well as a watertight seal to allow for the constant delivery of therapeutics to the ocular surface and surrounding periocular tissue. This study demonstrates the safety and utilization of the OWC on uninjured eyes and in our exposure keratopathy model. For initial safety studies (N = 3 per group), the eyelids remained intact and the eye uninjured. A blepharotomy (N = 6 per group) was performed to remove the upper and lower eyelids surrounding the left (OS) eye to create our exposure keratopathy model. Right (OD) eyes served as uninjured controls in all studies. Following OWC placement, 0.5 mL HPMC gel or balanced saline solution (BSS) was injected into the chamber. Animals were monitored daily and fully assessed via white light, fluorescein, and OCT imaging at least through 72 hours post OWC placement. In studies that included a blepharotomy, skin samples were analyzed by multiplex cytokine analysis. Results of safety experiments revealed no significant differences between treatment groups in corneal thickness, fluorescein staining, OCT imaging, or histological eye or skin sections when compared to control eyes. In our exposure keratopathy model, OWC treated eyes showed significantly less fluorescein uptake and also were found to have significantly lower levels of cytokines IL‐13 and IL‐5 in skin samples. These results demonstrate for the first time that treatment using the OWC device is not only safe, but significantly protects against blepharotomy‐induced exposure keratopathy. As a whole, this study advances our overall efforts to develop a feasible solution to treat ocular surface injuries, infections, and periocular burns.
A Local Search Approach to Observation Planning with Multiple UAVs

Observation planning for Unmanned Aerial Vehicles (UAVs) is a challenging task as it requires planning trajectories over a large continuous space and with motion models that can not be directly encoded into current planners. Furthermore, realistic problems often require complex objective functions that complicate problem decomposition. In this paper, we propose a local search approach to plan the trajectories of a fleet of UAVs on an observation mission. The strength of the approach lies in its loose coupling with domain specific requirements such as the UAV model or the objective function that are both used as black boxes. Furthermore, the Variable Neighborhood Search (VNS) procedure considered facilitates the adaptation of the algorithm to specific requirements through the addition of new neighborhoods. We demonstrate the feasibility and convenience of the method on a large joint observation task in which a fleet of fixed-wing UAVs maps wildfires over areas of a hundred square kilometers. The approach allows generating plans over tens of minutes for a handful of UAVs in matter of seconds, even when considering very short primitive maneuvers.
A study on relationship of job burnout and emotional intelligence with organizational commitment

Article history: Received May 16, 2013 Received in revised format 12 September 2013 Accepted 26 October 2013 Available online October 28 2013 This paper presents an empirical investigation on the effects of job burnout and emotional intelligence on organizational commitment in an Iranian Carbon producer. There are 300 full time employees and the study chooses a sample of 169 people. The proposed study adopts three well-known questionnaires for measuring job burnout, emotional intelligence and organizational commitment and Cronbach alphas are 0.766, 0.877 and 0.802, respectively. The study chooses regression analysis and other statistical observation to examine different hypotheses and the results indicate that there were some meaningful relationships among different components of the survey. In other words, when there was an increase on job burnout, we could expect less organizational commitment and while there was an increase on emotional intelligence, we may expect higher level of organizational commitment. © 2013 Growing Science Ltd. All rights reserved.
Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from Speech

Current approaches to speech emotion recognition focus on speech features that can capture the emotional content of a speech signal. Mel Frequency Cepstral Coefficients (MFCCs) are one of the most commonly used representations for audio speech recognition and classification. This paper proposes Gammatone Frequency Cepstral Coefficients (GFCCs) as a potentially better representation of speech signals for emotion recognition. The effectiveness of MFCC and GFCC representations are compared and evaluated over emotion and intensity classification tasks with fully connected and recurrent neural network architectures. The results provide evidence that GFCCs outperform MFCCs in speech emotion recognition.
Relationship between Physical Activity, Screen Time and Weight Status among Young Adolescents

It is well established that lack of physical activity and high bouts of sedentary behaviour are now associated with all-cause and cardiovascular mortality. The purpose of this study was to investigate the relationship between physical activity participation, overall screen time and weight status amongst early Irish adolescent youth. Participants were a sample of 169 students: 113 boys (mean age = 12.89 ± 0.34 years) and 56 girls (mean age = 12.87 ± 0.61 years). The data gathered in the present study included physical activity (accelerometry), screen time (self-report) and anthropometric measurements. Overweight and obese participants accumulated significantly more minutes of overall screen time daily compared to their normal-weight counterparts. A correlation between physical activity and daily television viewing was evident among girls. No significant interaction was apparent when examining daily physical activity and overall screen time in the prediction of early adolescents’ body mass index. Results suggest the importance of reducing screen time in the contribution towards a healthier weight status among adolescents. Furthermore, physical activity appears largely unrelated to overall screen time in predicting adolescent weight status, suggesting that these variables may be independent markers of health in youth. The existing relationship for girls between moderate-to-vigorous physical activity and time spent television viewing may be a potential area to consider for future intervention design with adolescent youth.
The Data in Your Hands: Exploring Novel Interaction Techniques and Data Visualization Approaches for Immersive Data Analytics

In this paper, we describe a concept for visualization and interaction with a large data set in an virtual environment. The core idea uses the traditional flat 2D representation as a base visualization but lets the user transform it into a spatial 3D visualizations on demand. Our visualization and interaction concept targets data analysts to use it for exploration and analysis, utilizing virtual reality to gain insight into complex data sets. The concept is based on the use of Parallel Sets for the representation of categorical data. By extending the conventional 2D Parallel Sets with a third dimension, correlations between path variables and the related number of items belonging to a specific node can be visualized. Furthermore, the concept uses virtual reality controllers in combination with a head-mounted display to control additional views. The purpose of the paper is to describe the core concepts and challenges for this type of spatial visualization and the related interaction design, including the use of gestures for direct manuipulation and a hand-attached menu for complex actions.
Bidirectional threshold voltage shift and gate leakage in 650 V p-GaN AlGaN/GaN HEMTs: The role of electron-trapping and hole-injection

The threshold voltage (VTH) instability of a 650 V p-GaN gate AlGaN/GaN HEMTs and its underlying physical mechanism was investigated by forward gate stress. A uniquely bidirectional shift in the VTH with the critical gate voltage Vcritical of 6 V was observed in the device after the static and dynamic gate stress. The temperature- and time-dependent gate leakage current revealed that the occurrence of electron-trapping and hole-injection in sequence with the increasing gate bias responsible for the inhomogeneous shift in VTH. At small positive gate bias (Vg<6V), the positive shift in VTH is induced by electron filling of acceptor-like traps in AlGaN barrier, while the gate leakage is accordingly dominated by trap-dominated SCLC. At large positive gate bias (VG>6V), the hole-injection is triggered that results in a negative shift in VTH and the gate leakage exhibits a substantial increase due to the forward turn on of the gate pn junction. Besides, the effective hole-injection also leads to a significant increase in OFF-state drain leakage, which is believed to be the pronounced electron-hole recombination in the channel.
Target Contrastive Pessimistic Discriminant Analysis

Domain-adaptive classifiers learn from a source domain and aim to generalize to a target domain. If the classifier's assumptions on the relationship between domains (e.g. covariate shift) are valid, then it will usually outperform a non-adaptive source classifier. Unfortunately, it can perform substantially worse when its assumptions are invalid. Validating these assumptions requires labeled target samples, which are usually not available. We argue that, in order to make domain-adaptive classifiers more practical, it is necessary to focus on robust methods; robust in the sense that the model still achieves a particular level of performance without making strong assumptions on the relationship between domains. With this objective in mind, we formulate a conservative parameter estimator that only deviates from the source classifier when a lower or equal risk is guaranteed for all possible labellings of the given target samples. We derive the corresponding estimator for a discriminant analysis model, and show that its risk is actually strictly smaller than that of the source classifier. Experiments indicate that our classifier outperforms state-of-the-art classifiers for geographically biased samples.
A novel method for natural motion mapping as a strategy of game immediacy

The method of operating a game could determine the psychological distance between the player and the game character, and thus, in the Virtual Reality, players’ control methodologies are important to enhance their immersion. This study has the objective of examining the difference in games according to the method of operation based on the player’s movements. This study researched the effect of the method of operating movement conforming to the movement of the character and the physical operation of the body on forming game experiences for the player. The result of performing an experiment increased reality for the game player through a controller in the shape of the actual control, to increase focus in the game. As so, game play through movements, including actual movements by the player displayed to enhance game satisfaction. In the part of media remediation field, Game can be defined as media which has their own unique hypermediacy. Especially, in the motion based game, players’ movement mediates players and the game, therefore, players’ movement could make players’ experience augmented or immediate in accordance with the characteristics of movements. Even though sports and dances genres of motion-based games are common, RPG or adventure genres are rare. It can be explained that the characteristics of the action have been explained in the immediacy. In a game of fantasy, which is difficult to experience in real-life situations, the nature of the player's motion can increase the immersion of the game, which can contribute to utilization of players’ motion and experience design in the various genres and suggestion of grounds theory. In addition, through this study, it is able to design motion-based games of various genres.
Effects of 8-hour time restricted feeding on body weight and metabolic disease risk factors in obese adults: A pilot study

BACKGROUND: Time restricted feeding decreases energy intake without calorie counting and may be a viable option for weight loss. However, the effect of this diet on body weight in obese subjects has never been examined. OBJECTIVE: This study investigated the effects of 8-h time restricted feeding on body weight and metabolic disease risk factors in obese adults. DESIGN: Obese subjects (n = 23) participated in an 8-h time restricted feeding intervention (ad libitum feeding between 10:00 to 18:00 h, water fasting between 18:00 to 10:00 h) for 12 weeks. Weight loss and other outcomes were compared to a matched historical control group (n = 23). RESULTS: Body weight and energy intake decreased in the time restricted group (–2.6% ± 0.5; –341 ± 53 kcal/d) relative to controls over 12 weeks (P < 0.05). Systolic blood pressure decreased in the time restricted feeding group (–7 ± 2 mm Hg) versus controls (P < 0.05). Fat mass, lean mass, visceral fat mass, diastolic blood pressure, LDL cholesterol, HDL cholesterol, triglycerides, fasting glucose, fasting insulin, HOMA-IR, and homocysteine were not significantly different from controls after 12 weeks (no group×time interaction). CONCLUSION: These findings suggest that 8-h time restricted feeding produces mild caloric restriction and weight loss, without calorie counting. It may also offer clinical benefits by reducing blood pressure.
BISMO: A Scalable Bit-Serial Matrix Multiplication Overlay for Reconfigurable Computing

Matrix-matrix multiplication is a key computational kernel for numerous applications in science and engineering, with ample parallelism and data locality that lends itself well to high-performance implementations. Many matrix multiplication-dependent applications can use reduced-precision integer or fixed-point representations to increase their performance and energy efficiency while still offering adequate quality of results. However, precision requirements may vary between different application phases or depend on input data, rendering constant-precision solutions ineffective. We present BISMO, a vectorized bit-serial matrix multiplication overlay for reconfigurable computing. BISMO utilizes the excellent binary-operation performance of FPGAs to offer a matrix multiplication performance that scales with required precision and parallelism. We characterize the resource usage and performance of BISMO across a range of parameters to build a hardware cost model, and demonstrate a peak performance of 6.5 TOPS on the Xilinx PYNQ-Z1 board.
A collaborative intrusion detection based on K-means and projective adaptive resonance theory

Since the start of the 21st century, computer networks have been through an exponential growth in terms of the network capacity, the number of the users and the type of tasks that are performed over the network. With the improving advanced technology of portable devices such as smart phones, tablet, smart devices, other computing devices, the number of network users are increasing more and more. Hence, security on network is very important for all net consumers. Intrusion detection is the process of defending intrusions. The action of entering to a system without permission is called intrusion. An intrusion detection system (IDS) can detect all incoming and out coming intrusion to the system. IDS can inspect all possible violations of a security guideline by monitoring system activities. Since IDS are fundamental part of security boundary, it can afford the ability to identify security holes in a system. There are many current methods used in intrusion detection. This paper analyzes the comparison between hybrid data mining methods and single method. The purpose of the system is to show that using hybrid data mining methods can reduce time complexity of the system than single method. This model was verified using KDD'99 data set. Experimental results clearly show that hybrid methods using K-means and Projective Adaptive Resonance Theory can significantly reduce model training time of the system and also it maintains the accuracy of detections.
Adaptive Contour Feature and Color Feature Fusion for Monocular Textureless 3D Object Tracking

Contour-matching-based textureless 3-D object tracking algorithms commonly use 3-D–2-D correspondence between the 3-D object model and 2-D object contour in the image to track the 3-D object. However, this often fails in highly cluttered backgrounds or in presence of motion blur. To overcome this problem, we propose a monocular textureless 3-D object tracking method based on adaptive fusion of contour feature and color feature. First, contour matching and local color statistics are performed nearby the projection contour of 3-D model to extract contour feature and color feature. Then, the energy function is defined based on adaptively weighted contour feature and statistical color feature, and the differentials of this energy function with respect to pose parameters of the 3-D object are derived. Finally, the optimal pose is obtained via LM solver. To deal with fast motion of object and camera, a coarse-to-fine tracking strategy is applied iteratively for multi-scale video frames. Qualitative and quantitative experiments show that the proposed algorithm has a great advantage over other state-of-the-art algorithms in the case of cluttered background and motion blur, and can obtain more accurate and robust tracking results.
High dynamic range Z-axis hybrid spring MEMS capacitive accelerometer

We report on a high-sensitivity, Z-axis capacitive accelerometer with high dynamic range. The high sensitivity is achieved by implementing a varying capacitive gap method, and by maintaining a narrow air gap of 2µm between the proof-mass and the stator plates. As Z-axis vertical accelerometers are very prone to stiction to the stator plates, we add a complementary set of serpentine reliability springs in order to control the displacement during high g (g=3D9.8m/s2) acceleration. It is found that using such reliability springs, there is a considerable improvement in the dynamic range, pull-in voltage, mode spacing and impact durability. The device is fabricated using an Integrated Inertial MEMS 9 DOF platform from GLOBALFOUNDRIES. Results indicate a static capacitance of 8.02pF, a pull-in voltage of 9 Volts, a sensitivity of 12 fF/g, and dynamic range of 450g.
Secure Image LBP Feature Extraction in Cloud-Based Smart Campus

The smart campus can monitor students in real time by analyzing students’ images, but a large number of images bring an unbearable burden to the smart campus. The convenience of cloud computing has attracted smart campus to outsource their huge amount of data to cloud servers. Although the outsourcing of data can reduce the computational and storage burden on smart campus, the privacy preserving becomes the biggest concern. This issue has attracted many researchers to study the protection of outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the local binary pattern (LBP) feature over huge encrypted images. The image owner uploads the encrypted version of images to the cloud. The cloud server takes the responsibility of extracting the LBP features from encrypted images for various applications. In the encryption process, an image is divided into non-overlapping blocks at first, and the blocks are shuffled to protect the image content. Next, all the non-center pixels in each block are shuffled. Finally, the pixels are encrypted by splitting the original image data randomly. When such an encrypted image is received, the cloud servers can calculate the LBP features by secure multiparty computation. The extracted features can be applied to many applications, such as texture classification, image retrieval, face recognition, and so on.
On r-Simple k-Path and Related Problems Parameterized by k/r

Abasi et al. (2014) and Gabizon et al. (2015) studied the following problems. In the $r$-Simple $k$-Path problem, given a digraph $G$ on $n$ vertices and integers $r,k$, decide whether $G$ has an $r$-simple $k$-path, which is a walk where every vertex occurs at most $r$ times and the total number of vertex occurrences is $k$. In the $(r,k)$-Monomial Detection problem, given an arithmetic circuit that encodes some polynomial $P$ on $n$ variables and integers $k,r$, decide whether $P$ has a monomial of degree $k$ where the degree of each variable is at most~$r$. In the $p$-Set $(r,q)$-Packing problem, given a universe $V$, positive integers $p,q,r$, and a collection $\cal H$ of sets of size $p$ whose elements belong to $V$, decide whether there exists a subcollection ${\cal H}'$ of $\cal H$ of size $q$ where each element occurs in at most $r$ sets of ${\cal H}'$. Abasi et al. and Gabizon et al. proved that the three problems are single-exponentially fixed-parameter tractable (FPT) when parameterized by $(k/r)\log r$, where $k=pq$ for $p$-Set $(r,q)$-Packing and asked whether the $\log r$ factor in the exponent can be avoided. We consider their question from a wider perspective: are the above problems FPT when parameterized by $k/r$ only? We resolve the wider question by (a) obtaining a $2^{O((k/r)^2\log(k/r))} (n+\log k)^{O(1)}$-time algorithm for $r$-Simple $k$-Path on digraphs and a $2^{O(k/r)} (n+\log k)^{O(1)}$-time algorithm for $r$-Simple $k$-Path on undirected graphs (i.e., for undirected graphs we answer the original question in affirmative), (b) showing that $p$-Set $(r,q)$-Packing is FPT, and (c) proving that $(r,k)$-Monomial Detection is para-NP-hard. For $p$-Set $(r,q)$-Packing, we obtain a polynomial kernel for any fixed $p$, which resolves a question posed by Gabizon et al. regarding the existence of polynomial kernels for problems with relaxed disjointness constraints.
Predictors of physical therapists’ intentions to counsel for smoking cessation: Implications for practice and professional education

ABSTRACT Objective This study explored factors predicting intention (predominant construct in social cognitive behavioral models) for smoking cessation (SC) counseling that may provide salient information for designing/targeting SC counseling training for physical therapists (PTs). Design: Cross-sectional questionnaire survey of PTs licensed to practice in Canada. Methods: Self-efficacy items and PT views were assessed for internal consistency (Cronbach’s α) and data reduced using principal axis factor analysis. Hierarchical linear regression modeling assessed predictors of intention to counsel for SC. Results: Internal consistency: self-efficacy and PT views: r = 0.937, r = 0.821, respectively. Factor structures from self-efficacy: “skills and knowledge” and “clinic incidentals” (57% total variance); from PT views’: “professional role” and “role modeling” (63.8% total variance). Significant predictors of intent to counsel for SC were “professional role” (β = 0.54, p ≤ 0.001), and “skills and knowledge” (β = 0.23, p ≤ 0.001). Conclusions: Physical therapists’ intent to engage in SC counseling increases when they consider it their “professional role. ” Encouraging PTs to view SC counseling as a professional role as well as increasing SC counseling self-efficacy focusing on skills and knowledge to do so needs to be incorporated into entry-level academic physical therapy programs and continuing professional PT education.