Miracle-dz/UniFAR-FTUs huggingface dataset:
Monochromatic covers in local edge colorings

An edge coloring of a graph is a local r-coloring if the edges incident to any vertex are colored with at most r distinct colors. In this paper, generalizing our earlier work, we study the following problem. Given a family of graphs F (for example matchings, paths, cycles, powers of cycles and paths, connected subgraphs) and fixed positive integers s, r, at least how many vertices can be covered by the vertices of no more than s monochromatic members of F in every local r-coloring of Kn. Several problems and results are presented. In particular, we prove the following two results. First, if n is sufficiently large then in any local r-coloring of the edges of Kn, the vertex set can be partitioned by the vertices of at most r monochromatic trees, which is sharp for local rcolorings (unlike for ordinary r-colorings according to the Ryser conjecture). Second, we show that we can partition the vertex set with at most O(r log r) monochromatic cycles in every local r-coloring of Kn. This answers a question of Conlon and Stein and slightly generalizes one of my favorite joint results with Endre (and with Gyárfás and Ruszinkó). Dedicated to Endre Szemerédi on the occasion of his 80th birthday
Untangling the effects of partner responsiveness on health and well-being: The role of perceived control

Perceived partner responsiveness (PPR)—the extent to which people feel understood, cared for, and appreciated—has been identified as an organizing principle in the study of close relationships. Previous work indicates that PPR may benefit physical health and well-being, but how PPR is associated with personal benefits is less clear. One cognitive mechanism that may help to explain these associations is perceived control. Here we tested two competing models (moderation vs. mediation) in which we assessed whether perceived control might explain how PPR impacts health, well-being, and mortality in a 20-year longitudinal study of adults (N = 1,186). We found that PPR has a long-term positive association with health, well-being, and mortality via increased perceived control and, in turn, decreased negative affect reactivity to daily stressors. These findings have important implications for understanding the cognitive mechanisms that link PPR to health and well-being.
Object tracking with similar background

In view of the boundary effect of the object tracking algorithm based on the correlation filter and the low tracking success rate in complicated cases, aobject tracking algorithm based on similar background and color histogram is proposed. First of all, through the cosine similarity, selecting the background area with high similarity to the object as the negative sample and training the correlation filter template and reduce the boundary effect. At the same time, we employ a color histogram basedBayes classifier to distinguish between the object and the background in complex cases. Finally, weprovide both quantitative and qualitative comparison of our approach with state-of-thearttrackers on the OTB-50 and OTB-100 video sets and the result improvesthat our tracker can effectively improve the accuracy and success rate of tracking, superior state-of-the-art tracking algorithms .
Prescribing patterns of antimicrobials in the Internal Medicine Department of Ibrahim Malik Teaching Hospital in Khartoum, 2016

Introduction Antimicrobials are among the most commonly prescribed therapeutic agents in hospitals. Irrational use of antimicrobials results in the development of antimicrobial resistance which could lead to life-threatening illnesses. Therefore, the assessment of antimicrobial prescribing and use is of utmost importance. This study aimed to examine the prescribing patterns of antimicrobials in the Internal Medicine Department of Ibrahim Malik Teaching Hospital in Khartoum, Sudan. Methods A descriptive, cross-sectional study was conducted using World Health Organization (WHO) indicators for antimicrobial use in hospitals. Systematic random sampling was used to select 245 medical records from the 2613 medical records of patients admitted to the internal medicine department in 2016. Data were collected using a data collection form and a structured interview with the chief pharmacist in the hospital. Results Of the 245 medical records examined, 201 (82%) patients were prescribed one or more antimicrobial drug. The average number of antimicrobials per patient was (2.1±1.1). The average duration of antimicrobial treatment was (4.9±3.8) days. The generic name was used in (35.6%) of antimicrobials, while (95.5%) of all antimicrobials were prescribed from the national essential medicines list. Overall, there were 421 courses of antimicrobials prescribed. The most frequently prescribed antimicrobials were ceftriaxone (131 courses) and metronidazole (89 courses). Among the documented infectious diseases, the most frequently encountered was pneumonia, followed by malaria. There was no drug and therapeutic committee, hospital formulary or essential medicines list, and standard treatment guidelines for infectious diseases in the hospital. Conclusion The results of the study revealed a high percentage of antimicrobial use in the Internal Medicine Department. Multifaceted interventions are urgently needed to promote rational prescribing of antimicrobials.
HPMC/PVP Dissolving Microneedles: a Promising Delivery Platform to Promote Trans-Epidermal Delivery of Alpha-Arbutin for Skin Lightening

Alpha-arbutin is one of the most efficient skin lightener agents, which shows the effect on reducing the pigmentation by competitively inhibiting human tyrosinase. However, alpha-arbutin has difficulty in skin permeability due to its hydrophilic property. The objective of this study was, therefore, to develop alpha-arbutin-loaded dissolving microneedles (DMNs) for improving the delivery of alpha-arbutin into the skin. The DMN patch was prepared using Gantrez™ S-97, hydroxypropyl methylcellulose (HPMC), polyvinylpyrrolidone K-90 (PVP), chitosan, and their combinations. The optimal 8% alpha-arbutin-loaded DMNs, aside from Gantrez™ S-97, was successfully formulated with combination of 8% w/w HPMC and 40% w/w PVP K-90 (HPMC/PVP) at the weight ratio of 1:1. Both DMNs had 100% of penetration into porcine skin. Over 12 h of skin permeation, the flux of Gantrez™ S-97 DMNs and the HPMC/PVP DMNs were 66.21 μg/cm2/h and 74.24 μg/cm2/h, respectively. The accumulation amount of alpha-arbutin in the skin from Gantrez™ S-97 DMNs and HPMC/PVP DMNs was 107.76 μg and 312.23 μg, respectively. In comparison to the gel formulations, Gantrez™ S-97 DMNs and HPMC/PVP DMNs increase the delivery of alpha-arbutin across the skin approximately 2 and 4.7 times, respectively. In vivo studies found that alpha-arbutin-loaded HPMC/PVP DMNs delivered more alpha-arbutin into the skin than commercial cream. Moreover, the skin can reseal naturally after removal of DMNs patch without any signs of infection and remain stable in accelerated conditions for 4 weeks. Accordingly, alpha-arbutin-loaded HPMC/PVP DMNs could be a promising delivery platform for promoting trans-epidermal delivery of alpha-arbutin for skin lightening.
FFTE on SVE: SPIRAL-Generated Kernels

In this paper we propose an implementation of the fast Fourier transform (FFT) targeting the ARM Scalable Vector Extension (SVE). We performed automatic vectorization via a compiler and an explicit vectorization through code generation by SPIRAL for FFT kernels, and compared the performance. We show that the explicit vectorization of SPIRAL generated code improves performance significantly. Performance results of FFTs on RIKEN's Fugaku processor simulator are reported. With the ARM compiler SPIRAL-generated FFT kernels written in SVE intrinsic are up to 3.16 times faster than FFT kernels of FFTE written in Fortran and up to 5.62 times faster than SPIRAL-generated FFT kernels written in C.
IDAT at FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets

This overview paper describes the first shared task on irony detection for the Arabic language. The task consists of a binary classification of tweets as ironic or not using a dataset composed of 5, 030 Arabic tweets about different political issues and events related to the Middle East and the Maghreb. Tweets in our dataset are written in Modern Standard Arabic but also in different Arabic language varieties including Egypt, Gulf, Levantine and Maghrebi dialects. Eighteen teams registered to the task among which ten submitted their runs. The methods of participants ranged from feature-based to neural networks using either classical machine learning techniques or ensemble methods. The best performing system achieved F-score value of 0.844, showing that classical feature-based models outperform the neural ones.
Toward Scalable Measures of Quality of Interaction

Motor resonance, the activation of an observer’s motor control system by another actor’s movements, has been claimed to be an indicator for quality of interaction. Motor interference as one of the consequences of the presence of resonance can be detected by analyzing an actor’s spatial movements. It has therefore been used as an indicator for the presence of motor resonance. Unfortunately, the experimental paradigm in which motor interference has been shown to be detectable is ecologically implausible both in terms of the types of movements employed and the number of repetitions required. In the presented experiment, we tested whether some of these experimental constraints can be relaxed or modified toward a more naturalistic behavior without losing the ability to detect the interference effect. In the literature, spatial variance has been analytically quantified in many different ways. This study found these analytical variations to be nonequivalent by implementing them. Back-and-forth transitive movements were tested for motor interference; the effect was found to be more robust than with left-right movements, although the direction of interference was opposite to that reported in the literature. We conclude that motor interference, when measured by spatial variation, lacks promise for embedding in naturalistic interaction scenarios because the effect sizes were small.
CityGuard

Forecasting the fire risk is of great importance to fire prevention deployments in a city, which can reduce loss even deaths caused by fires. However, it is very challenging because fires are influenced by many complex factors, including spatial correlations, temporal dependencies, even the mixture of these two and external factors. Firstly, the fire risk of a region is influenced by temporal effect of internal factors (e.g., the historical fire risk records) and temporal effect of external factors (e.g., weather). Secondly, a region's fire risk is not only influenced by its inherent geospatial attributes (e.g., POIs) but also dependent on other regions in spatial. To address these challenges, we propose a machine learning approach to forecast the fire risk, entitled NeuroFire. NeuroFire can represent internal and external temporal effect then combine the temporal representation and spatial dependencies by a spatial-temporal loss function. Experimental evaluations on real-world datasets show that our NeuroFire outperforms 9 baselines, demonstrating the performance of our approach by several visualizations. Moreover, we implement a citywide fire forecasting system named CityGuard to display the analysis and forecasting results, which can assist the fire rescue department in deploying fire prevention.
Probing Sucrose Contents in Everyday Drinks Using Miniaturized Near-Infrared Spectroscopy Scanners

Near-Infrared Spectroscopy (NIRS) is a non-invasive sensing technique which can be used to acquire information on an object's chemical composition. Although NIRS is conventionally used in dedicated laboratories, the recent introduction of miniaturized NIRS scanners has greatly expanded the use cases of this technology. Previous work from the UbiComp community shows that miniaturized NIRS can be successfully adapted to identify medical pills and alcohol concentration. In this paper, we further extend this technology to identify sugar (sucrose) contents in everyday drinks. We developed a standalone mobile device which includes inter alia a NIRS scanner and a 3D printed clamp. The clamp can be attached to a straw-like tube to sense a liquid's sucrose content. Through a series of studies, we show that our technique can accurately measure sucrose levels in both lab-made samples and commercially available drinks, as well as classify commercial drinks. Furthermore, we show that our method is robust to variations in the ambient temperature and lighting conditions. Overall, our system can estimate the concentration of sugar with ±0.29 g/100ml error in lab-made samples and < 2.0 g/100ml error in 18 commercial drinks, and can identify everyday drinks with > 99% accuracy. Furthermore, in our analysis, we are able to discern three characteristic wavelengths in the near-infrared region (1055 nm, 1235 nm and 1545 nm) with acute responses to sugar (sucrose). Our proposed protocol contributes to the development of everyday "food scanners" consumers.
Personalized Context-aware Collaborative Online Activity Prediction

With the rapid development of Internet services and mobile devices, nowadays, users can connect to online services anytime and anywhere. Naturally, user's online activity behavior is coupled with time and location contexts and highly influenced by them. Therefore, personalized context-aware online activity modelling and prediction is very meaningful and necessary but also very challenging, due to the complicated relationship between users, activities, spatial and temporal contexts and data sparsity issues. To tackle the challenges, we introduce offline check-in data as auxiliary data and build a user-location-time-activity 4D-tensor and a location-time-POI 3D-tensor, aiming to model the relationship between different entities and transfer semantic features of time and location contexts among them. Accordingly, in this paper we propose a transfer learning based collaborative tensor factorization method to achieve personalized context-aware online activity prediction. Based on real-world datasets, we compare the performance of our method with several state-of-the-arts and demonstrate that our method can provide more effective prediction results in the high sparsity scenario. With only 30% of observed time and location contexts, our solution can achieve 40% improvement in predicting user's Top5 activity behavior in new time and location scenarios. Our study is the first step forward for transferring knowledge learned from offline check-in behavior to online activity prediction to provide better personalized context-aware recommendation services for mobile users.
Diamond matrix powers kernels

Matrix powers kernel calculates the vectors Akv, for k = 1, 2,. , m and they are the heart of various scientific computations, including communication avoiding iterative solvers. In this paper we propose diamond matrix powers kernel - DMPK, which has the purpose to apply the "diamond tiling" stencil algorithm to general matrices. It can also be considered as an extension of the PA1 and PA2 algorithms, introduced by Demmel et al. Our approach enables us to control the balance between the amount of communication avoidance and redundant computation inherently present in communication avoiding algorithms. We present a proof of concept implementation of the algorithm using MPI routines. The experiments we performed show that the control of the amount of computation and communication is achievable, and with more thorough optimisations, DMPK is a promising alternative to existing MPK approaches.
Data-Driven Battery-Lifetime-Aware Scheduling for Electric Bus Fleets

Electric vehicles (EVs) have experienced a sensational growth in the past few years, due to the potential of mitigating global warming and energy scarcity problems. However, the high manufacturing cost of battery packs and limited battery lifetime hinder EVs from further development. Especially, electric bus, as one of the most important means of public transportation, suffers from long daily operation time and peak-hour passenger overload, which aggravate its battery degradation. To address this issue, we propose a novel data-driven battery-lifetime-aware electric bus scheduling system. Leveraging practical bus GPS and transaction datasets, we conduct a detailed analysis of passenger behaviors and design a reliable prediction model for passenger arrival rate at each station. By taking passenger waiting queue at each bus station analogous to data buffer in network systems, we apply Lyapunov optimization and obtain an electric bus scheduling strategy with reliable performance guarantee on both battery degradation rate and passengers' service quality. To verify the effectiveness of the system, we evaluate our design on a 12-month electric bus operation datasets from the city of Shenzhen. The experimental results show that, compared with two baseline methods, our system reduces the battery degradation rate by 14.3% and 21.7% under the same passenger arrival rate, while preserving good passenger service quality.
Designing and Evaluating a Customizable Head-mounted Vision Enhancement System for People with Low Vision

Recent advances in head-mounted displays (HMDs) present an opportunity to design vision enhancement systems for people with low vision, whose vision cannot be corrected with glasses or contact lenses. We aim to understand whether and how HMDs can aid low vision people in their daily lives. We designed ForeSee, an HMD prototype that enhances people’s view of the world with image processing techniques such as magnification and edge enhancement. We evaluated these vision enhancements with 20 low vision participants who performed four viewing tasks: image recognition and reading tasks from near- and far-distance. We found that participants needed to combine and adjust the enhancements to comfortably complete the viewing tasks. We then designed two input modes to enable fast and easy customization: speech commands and smartwatch-based gestures. While speech commands are commonly used for eyes-free input, our novel set of onscreen gestures on a smartwatch can be used in scenarios where speech is not appropriate or desired. We evaluated both input modes with 11 low vision participants and found that both modes effectively enabled low vision users to customize their visual experience on the HMD. We distill design insights for HMD applications for low vision and spur new research directions.
Beyond separation of duty: an algebra for specifying high-level security policies

A high-level security policy states an overall requirement for a sensitive task. One example of a high-level security policy is a separation of duty policy, which requires a sensitive task to be performed by a team of at least k users. It states a high-level requirement about the task without the need to refer to individual steps in the task. While extremely important and widely used, separation of duty policies state only quantity requirements and do not capture qualification requirements on users involved in the task. In this paper, we introduce a novel algebra that enables the specification of high-level policies that combine qualification requirements with quantity requirements motivated by separation of duty considerations. A high-level policy associates a task with a term in the algebra and requires that all sets of users that perform the task satisfy the term. We give the syntax and semantics of the algebra and study algebraic properties of its operators. We also study several computational problems related to the algebra.
An Image Cues Coding Approach for 3D Human Pose Estimation

Although Deep Convolutional Neural Networks (DCNNs) facilitate the evolution of 3D human pose estimation, ambiguity remains the most challenging problem in such tasks. Inspired by the Human Perception Mechanism (HPM), we propose an image-to-pose coding method to fill the gap between image cues and 3D poses, thereby alleviating the ambiguity of 3D human pose estimation. First, in 3D pose space, we divide the whole 3D pose space into multiple subregions named pose codes, turning a disambiguation problem into a classification problem. The proposed coding mechanism covers multiple camera views and provides a complete description for 3D pose space. Second, it is noteworthy that the articulated structure of the human body lies on a sophisticated product manifold and the error accumulation in the chain structure will undoubtedly affect the coding performance. Therefore, in image space, we extract the image cues from independent local image patches rather than the whole image. The mapping relationship between image cues and 3D pose codes is established by a set of DCNNs. The image-to-pose coding method transforms the implicit image cues into explicit constraints. Finally, the image-to-pose coding method is integrated into a linear matching mechanism to construct a 3D pose estimation method that effectively alleviates the ambiguity. We conduct extensive experiments on widely used public benchmarks. The experimental results show that our method effectively alleviates the ambiguity in 3D pose recovery and is robust to the variations of view.
The Takeoff in the Long Jump & Other Running Jumps

Despite its importance to success in the long jump, the takeoff has been accorded little attention by sports biomechanists. We conducted a study to determine the characteristics of an athlete's technique that determine the vertical velocity gained during the takeoff. We found that the vasti, soleus and gastrocnemius experienced a lengthening-shortening sequence of actions during the takeoff and that enhancement due to use of the stretch-shorten cycle did not make a significant contribution to the vertical velocity developed via these muscles. Standing and running vertical jumps from a two-feet takeoff were found to involve different modes of muscle action. Running long and vertical jumps and sprinting involve almost identical modes of muscle action. The results of our study of the long jump appear, therefore, to apply to running jumps in general and to sprint running.
Socioeconomic status, perceptions and obesity among adolescents in Jordan

Introduction Prevalence of obesity among adolescents constitutes a serious public health problem. We aimed to investigate the association between socioeconomic factors and obesity perceptions, and prevalence of overweight and obesity among school adolescents. Methods A cross-sectional study that included students 12 to 17 years old participating from six schools that were randomly selected in Northern Jordan. Body mass index (BMI) measurements and interview questions were completed by trained researchers. A total of 701 were successfully involved in the analysis. Results Students involved were 53.6% males and 46.4% females. Students with normal BMI had a mean BMI of 21.45 (+1.76). Those who were obese, or overweight were 202 (28.8%) students and had a BMI of 29.1 (+4.05). Family monthly income, mother's education and working status among other socioeconomic status factors were significantly associated with children’s overweight or obesity. Perceptions about obesity including meal choices, interest in self body weight, considering someone in family as obese and instructions at school were statistically significant as well. Conclusion Family factors, adolescents' pocket money and perceptions about obesity were significant predictors of obesity among adolescents in Jordan. Effective intervention strategies should be implemented in schools and other primary care settings to reduce the relatively high prevalence of adolescent's obesity observed in this study.