Miracle-dz/UniFAR-FTUs huggingface dataset:
Grit and Academic Success in Graduate Masters-Level Athletic Training Programs

: A construct termed grit, a non-cognitive personality trait, defined as the perseverance and passion for achieving long-term goals, was introduced by Duckworth et al. Grit entails working tirelessly to reach goals while maintaining determination and interest despite obstacles that hinder progress. Although grit-scale surveys are included as part of the application process in other healthcare professional education programs, no published literature cites grit-scale surveys as part of the applications process in Masters-level athletic training (MSAT) programs. The problem addressed is the current lack of gritscale utilization during the admission process to MSAT programs as a non-cognitive tool to contribute to identifying applicants who will be academically successful. The National Athletic Trainers’ Association Research Survey Service was utilized to send a web-based survey to over 1000 email addresses. Data was analyzed using the most current version of SPSS (26.0) and was analyzed over four stages. All variables in the study were examined using descriptive statistics. Examples of descriptive statistical analysis used were mean and standard deviation, nominal/ordinal level frequency and percentage of categorical variables, and interval and ratio levels of values for continuous variables. Results of the study uncovered that Grit-S scores were higher in participants who attempted to complete the BOC exam more than once. Grit-S scores were lower in participants who had experienced severe human suffering. No demographic variable (age, racial and ethnic identity, gender) was found to correlate with the Grit-S score. Although the first-time pass rate of the BOC exam was not positively correlated with a higher grit score, Grit continues to reveal itself as a positive indicator of academic success. To maintain and increase alignment with other healthcare professional education programs, the use of Grit-S assessments during the admissions process as an identifier of applicants who will maintain determination and interest, despite failures, setbacks, and plateaus is recommended.
EasyChair Preprint No 1375 Aiding Intra-Text Representations with Visual Context for Multimodal Named Entity Recognition

With the massive explosion of social media platforms such as Twitter and Instagram, people everyday share billions of multimedia posts, containing images and text. Typically, text in these posts is short, informal and noisy, leading to ambiguities which can be resolved using images. In this paper we will explore text-centric Named Entity Recognition task on these multimedia posts. We propose an end to end model which learns a joint representation of a text and an image. Our model extends multi-dimensional self-attention technique, where now image helps to enhance relationship between words. Experiments show that our model is capable of capturing both textual and visual contexts with greater accuracy, achieving state-of-the-art results on Twitter multimodal Named Entity Recognition dataset.
F-LaaS: A Control-Flow-Attack Immune License-as-a-Service Model

We use license servers to verify users' credentials and to restrict access to proprietary software. Due to logistical reasons, it is often economical to use third-party servers to manage licenses. Sadly, users on client machines can mount sophisticated attacks on the executables and try to circumvent the license check. This can be used to crack the software, and thus it is necessary for software writers to prevent such attacks, which include the use of additional code to check the integrity of the binary and the control flow. In spite of such techniques, modern control flow bending(CFB) techniques that rely on running instrumented binaries on virtual machines can circumvent such checks and change the behavior of branches and jumps at runtime. They are however extremely computationally inefficient. We propose an AI-based technique that is an order of magnitude faster than the state-of-the-art and show its efficacy by breaking three widely used license managers, and five popularly used software. Finally, we propose a new license management service, F-LaaS, which hides key functions in the binary. These functions are downloaded at runtime upon the successful verification of the license. We show that the mean performance overhead of F-LaaS is negligible: 0.26%.
An Intermediate Representation for Optimizing Machine Learning Pipelines

Machine learning (ML) pipelines for model training and validation typically include preprocessing, such as data cleaning and feature engineering, prior to training an ML model. Preprocessing combines relational algebra and user-defined functions (UDFs), while model training uses iterations and linear algebra. Current systems are tailored to either of the two. As a consequence, preprocessing and ML steps are optimized in isolation. To enable holistic optimization of ML training pipelines, we present Lara, a declarative domain-specific language for collections and matrices. Lara's inter-mediate representation (IR) reflects on the complete program, i.e., UDFs, control flow, and both data types. Two views on the IR enable diverse optimizations. Monads enable operator pushdown and fusion across type and loop boundaries. Combinators provide the semantics of domain-specific operators and optimize data access and cross-validation of ML algorithms. Our experiments on preprocessing pipelines and selected ML algorithms show the effects of our proposed optimizations on dense and sparse data, which achieve speedups of up to an order of magnitude.
Hourly probabilistic snow forecasts over complex terrain: a hybrid ensemble postprocessing approach

Abstract. Accurate and high-resolution snowfall and fresh snow forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. In this article a new hybrid statistical postprocessing method is proposed, which combines standardized anomaly model output statistics (SAMOS) with ensemble copula coupling (ECC) and a novel re-weighting scheme to produce spatially and temporally high-resolution probabilistic snow forecasts. Ensemble forecasts and hindcasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) serve as input for the statistical postprocessing method, while measurements from two different networks provide the required observations.This new approach is applied to a region with very complex topography in the eastern European Alps. The results demonstrate that the new hybrid method allows one not only to provide reliable high-resolution forecasts, but also to combine different data sources with different temporal resolutions to create hourly probabilistic and physically consistent predictions.
Unbounded Stress in Subregular Phonology

This paper situates culminative unbounded stress systems within the subregular hierarchy for functions. While Baek (2018) has argued that such systems can be uniformly understood as input tier-based strictly local constraints, we show here that default-to-opposite-side and default-to-same-side stress systems belong to distinct subregular classes when they are viewed as functions that assign primary stress to underlying forms. While the former system can be captured by input tier-based input strictly local functions, a subsequential function class that we define here, the latter system is not subsequential, though it is weakly deterministic according to McCollum et al.’s (2018) non-interaction criterion. Our results motivate the extension of recently proposed subregular language classes to subregular functions and argue in favor of McCollum et al’s definition of weak determinism over that of Heinz and Lai (2013).
Harnessing the power of data to support community‐based research

Volumes of data are generated at every moment as we go through the paces of our daily lives. Many of these data flows are routinely captured through administrative records, social media, and surveys. Historically, agencies at different levels of government have been responsible for curating and reporting statistics about our social, economic, and health conditions associated with these data flows. Recently, the U.S. government has proposed the use of data derived from administrative records at the federal level to support social policy and program evaluation. Why not consider parallel activities at state and local levels? Harnessing local data sources and integrating them with state and federal sources will provide timelier and more geographically specific analyses to support local insights and policy development. Leveraging community‐based participatory research, researchers and civic leaders can work together to identify the questions and execute rigorous, yet flexible, processes for building local sustainable community learning cultures based on data‐driven discovery. In the process of conducting research with local civic leaders, we have observed that issues can be classified into 3 categories: locating and describing a population within a community; estimating a statistic and a measure of variability; and evaluating a program, policy, or procedure. Through a series of case studies, this paper demonstrates the unexpected value in liberating and repurposing local data.
A Hyper-heuristic For The DialA-Ride Problem With Time Windows

The Dial-a-Ride Problem with time Windows (DARPTW) is a combinatorial optimization problem related to transportation, in which a set of customers must be picked up from an origin location and they have to be delivered to a destination location. A transportation schedule must be constructed for a set of available vehicles, and several constraints have to be considered, particularly time windows, which define an upper and lower time bound for each customer request in which a vehicle must arrive to perform the service. Because the complexity of DARPTW, a number of algorithms have been proposed for solving the problem, mainly based on metaheuristics such as Genetic Algorithms and Simulated Annealing. In this work, a different approach for solving DARPTW is proposed, designed and evaluated: hyper-heuristics, which are alternative heuristic methods that operate at a higher abstraction level than metaheuristics, because rather than searching in the problem space directly, they search in a space of low-level heuristics to find the best strategy through which good solutions can be found. Although the proposed hyper-heuristic uses simple and easy-toimplement operators, the experimental results demonstrate efficient and competitive performance on DARPTW when compared to other metaheuristics from the literature.
PERFORMANCE APPRAISAL PROCESS IN LIBYAN PUBLIC ORGANISATIONS : AN EXPLORATORY STUDY

The purpose of this study is to explore the underlying practices of the performance appraisal system (PAS) in the Libyan public sector. In doing so, the study illuminates the influence of cultural practices on the PA system. Looking from an interpretive lens, it explores those factors that help Libyan managers to make judgments about their employees’ performance. In addition, it explores how company managers perceive the meaning of their work before, during and after the 2011 uprising in Libya (17 of February revolution). This study provides empirical insights of 30 managers at the Ahlia Cement Company (ACC). A qualitative approach was employed; data was gathered through semi-structured interviews, documentation and direct observation, and thematic analysis was used in analysing the collected data. The findings reveal employees were not very clear about the aims of their PA system, and the Libyan culture plays a significant role in the implementation of management practices in organizations, including performance appraisal, as managers take their decisions based on their relationships with their employees, whether friendship or family related. The study uncovers a sharp contrast between that two periods – the period of uprising and the period after the uprising.
A Modified Strength Pareto Evolutionary Algorithm 2 based Environmental /Economic Power Dispatch

A Strength Pareto Evolutionary Algorithm 2 (SPEA 2) approach for solving the multi-objective Environmental / Economic Power Dispatch (EEPD) problem is presented in this paper. In the past fuel cost consumption minimization was the aim (a single objective function) of economic power dispatch problem. Since the clean air act amendments have been applied to reduce SO2 and NOX emissions from power plants, the utilities change their strategies in order to reduce pollution and atmospheric emission as well, adding emission minimization as other objective function made economic power dispatch (EPD) a multi-objective problem having conflicting objectives. SPEA2 is the improved version of SPEA with better fitness assignment, density estimation, and modified archive truncation. In addition fuzzy set theory is employed to extract the best compromise solution. Several optimization run of the proposed method are carried out on 3-units system and 6-units standard IEEE 30-bus test system. The results demonstrate the capabilities of the proposed method to generate well-distributed Pareto-optimal non-dominated feasible solutions in single run. The comparison with other multi-objective methods demonstrates the superiority of the proposed method.
Effects of cooking time , temperature , and salt concentration on the phenolic content and antioxidant activity of selected edible mushrooms

People nowadays recognize the beneficial effects of antioxidants to health; hence, many prefer consuming foods which contain phytochemicals known for their antioxidant properties. Mushroom is one among the popular sources of natural antioxidants. However, cooking factors such as cooking method, temperature, and other cooking ingredients, could affect the level of phytochemicals and antioxidant activity in cooked food. To determine the effect of cooking time, temperature, and salt concentration on the antioxidant activity and total phenolic content of edible mushroom, four species were used in this study namely, Agaricus bisporus, Pleurotus florida, Pleurotus ostreatus and Termitomyces cartilaginous. Methanolic extracts from the four edible mushrooms were analyzed for phenolic content and antioxidant activity after subjecting to different combinations of cooking time, temperature, and salt concentration. Results showed that A. bisporus contain the highest phenolic content (2.84±0.47 mg CE/100g) while T. Cartilaginous had the highest antioxidant activity (76.67±1.50 % DPPH scavenging activity). The length of cooking time and salt concentration did not significantly affect the levels of phenolics and antioxidant activity. On the other hand, increase in temperature led to reduced level of phenolic content but increased antioxidant activity. In conclusion, boiling mushroom for ≤5 minutes with salt cannot be said that affect phenolic content and antioxidant activity. The use of high temperature in cooking mushroom can be both beneficial and disadvantageous. Boiling mushrooms is not recommended as it decreases the amount of phenolic compounds however; it can increase the antioxidant activity.
Sensor-Based Signer Independent Continuous Arabic Sign Language Recognition

A Master of Science thesis in Mechatronics Engineering by Mohamed Hassan entitled, "Sensor-Based Signer Independent Continuous Arabic Sign Language Recognition," submitted in May 2017. Thesis advisor is Dr. Khaled Assaleh and thesis co-advisor is Dr. Tamer Shanableh. Soft and hard copy available.
Chronic fl uoxetine treatment increases NO bioavailability and calcium-sensitive potassium channels activation in rat mesenteric resistance arteries

Fluoxetine, a selective serotonin reuptake inhibitor (SSRI), has effects beyond its antidepressant properties, altering, e.g., mechanisms involved in blood pressure and vasomotor tone control. Although many studies have addressed the acute impact of fluoxetine on the cardiovascular system, there is a paucity of information on the chronic vascular effects of this SSRI. We tested the hypothesis that chronic fluoxetine treatment enhances the vascular reactivity to vasodilator stimuli by increasing nitric oxide (NO) signaling and activation of potassium (Kþ) channels. Wistar rats were divided into two groups: (I) vehicle (water for 21 days) or (II) chronic fluoxetine (10 mg/kg/day in the drinking water for 21 days). Fluoxetine treatment increased endothelium-dependent and independent vasorelaxation (analyzed by mesenteric resistance arteries reactivity) as well as constitutive NO synthase (NOS) activity, phosphorylation of eNOS at Serine and NO production, determined by western blot and fluorescence. On the other hand, fluoxetine treatment did not alter vascular expression of neuronal and inducible NOS or guanylyl cyclase (GC). Arteries from fluoxetine-treated rats exhibited increased relaxation to pinacidil. Increased acetylcholine vasorelaxation was abolished by a calcium-activated Kþ channel (KCa) blocker, but not by an inhibitor of KATP channels. On the other hand, vascular responses to Bay 41-2272 and 8-bromo-cGMP were similar between the groups. In conclusion, chronic fluoxetine treatment increases endotheliumdependent and independent relaxation of mesenteric resistance arteries by mechanisms that involve increased eNOS activity, NO generation, and KCa channels activation. These effects may contribute to the cardiovascular effects associated with chronic fluoxetine treatment. & 2015 Elsevier B.V. All rights reserved.
TUNING PID CONTROLLERS USING HYBRID GENETIC AND NELDER-MEAD ALGORITHM

This Paper presents a hybrid genetic and Nelder-Mead algorithm (HGNMA) to determine the optimal proportional-integral-derivative (PID) controller parameters. PID controller exhibits good features, including easy implementation, stable convergence characteristic and good computational efficiency. The HGNMA algorithm is implemented in MATLAB, the results obtained from this hybrid technique is compared with that obtained from Genetic Algorithm (GA) and the conventional PID which is tuned via the Ziegler-Nichols frequency response method. The proposed method is more efficient in improving the step response characteristics such as, reducing rise time, settling time and maximum overshoot.
Frequency of non-invasive fungal sinusitis in patients undergoing surgery for chronic rhinosinusitis

Background: In this study, we investigated the presence of non-invasive fungal sinusitis in patients who underwent endoscopic sinus surgery with the diagnosis of chronic rhinosinusitis in our clinic. Methods: A total of 283 patients who underwent functional endoscopic sinus surgery for chronic rhinosinusitis in our clinic between 2008-2018, who were at the ages of 18 or above, and who did not have invasive fungal sinusitis and tumor were included in the study. Sinus pathology was examined by routine examination using nasal endoscopy and computed tomography. Eosinophil count, total, and specific IgE levels were measured in the peripheral blood samples of the patients. Sinus surgery was performed under general and local anesthesia with the Messerklinger technique. Aspirated secretions from the sinuses and removed surgical tissues were examined microbiologically and histopathologically. Results: 68 females, 215 males, totally 283 patients with a mean age of 41 had sinus surgery; 11 (40.74%) females and 16 (59.26%) males, totally 27 (9.54%) patients had non-invasive fungal sinusitis. 18 (6.36%) of the cases were microbiologically reported as allergic fungal sinusitis while 9 (3.18%) of them reported as mycetoma. Nasal obstruction (19, 70.37%) and headache (13, 48.14%) were the most common clinical findings. Eosinophilic mucus was detected in the sinus material of the patients with allergic fungal sinusitis. According to their prevalence, Aspergillus, Penicillium, and Candida were the most prevalent in the patients with allergic fungal sinusitis, respectively; Aspergillus fumigatus and Aspergillus flavus were the most prevalent in the patients with allergic fungal sinusitis, respectively. Radiologically, fungal balls localized at the maxillary sinuses in six patients, the sphenoid sinuses in two patients, and the ethmoid sinus in one patient. As a treatment, sinus surgery was performed as a complete evacuation of the mucus and removal of pathological and polypoid tissues. The follow-up periods of the patients ranged between 7-31 months; the mean duration of follow-ups was 17 months. Non-invasive fungal infections were not treated with antifungal therapy, no recurrence was observed. Conclusions: In this study, non-invasive fungal sinus infection was detected 9.54% of patients. Nasal examination, radiological, and immunological examination should be applied to patients as additional examinations on the suspicion of pre-operative fungal rhinosinusitis; samples should be taken from the sinuses during surgery and should be examined for fungus microbiologically and histopathologically. Antifungal therapy was not administered to patients with non-invasive fungal infection. Endoscopic sinus surgery is a successful method in the treatment of non-invasive fungal rhinosinusitis.
Usefulness of oils for cleaning the host matrix and for cryoprotection of lipidic cubic phase crystals

The lipidic cubic phase method is an effective approach for membrane protein crystallography. The in meso grown crystals are usually cryocooled directly without removing the host matrix from the harvested crystal surface. However, the host matrix often causes the appearance of scattering rings and an increase in background scattering during the data collection. Moreover, the frozen host matrix sometimes becomes opaque and it can hinder conventional crystal centering. In this study, several oils were examined for their ability to clean the host matrix and to provide cryoprotection for crystals grown in the lipidic cubic phase. Several of the tested oils appeared to be useful in terms of their effect on crystal stability and background scattering. This method should be of value for the collection of highly accurate data sets.
Studying Semantic Chain Shifts with Word2Vec: FOOD>MEAT>FLESH

Word2Vec models are used to study the semantic chain shift FOOD>MEAT>FLESH in the history of English, c. 1425-1925. The development stretches out over a long time, starting before 1500, and may possibly be continuing to this day. The semantic changes likely proceeded as a push chain.
Distributed control system for active mirrors

This paper presents the IAC (Instituto de Astrofisica de Canaries, Spain) proposal of a distributed control system intended for the active support of a 8 m mirror. The system incorporates a large number of compact `smart' force actuators, six force definers, and a mirror support computer (MSC) for interfacing with the telescope control system and for general housekeeping. We propose the use of a network for the interconnection of the actuators, definers and the MSC, which will minimize the physical complexity of the interface between the mirror support system and the MSC. The force actuator control electronics are described in detail, as is the system software architecture of the actuator and the MSC. As the network is a key point for the system, we also detail the evaluation of three candidates, before electing the CAN bus.
The Impact of Width Granularity on FinFET Latch Operation and Optimization

This paper investigates the effects of coarse width granularity on latch circuit designs; such granularity may be imposed by FinFET technology. We analyze the sensitivities of delay, power and stability on latch sizing. A performance metric to evaluate Quantization Error (QE) is defined, and used to optimize width granularity in FinFET circuits. Simulation results show that logic latch structure achieves better-optimized performance given its larger sizing compared to that of the General Purpose Register (GPR) latch, while both types of latches operate satisfactorily in a FinFET implementation.