Miracle-dz/UniFAR-FTUs huggingface dataset:
An MDP-Based Wireless Energy Harvesting Decision Strategy for Mobile Device in Edge Computing

Edge computing has been regarded as an ideal platform to provision computation and storage resources at the network edge thanks to its vast distribution and user proximity. Owing to such features, it is also promising to provision energy resources at the network edge via wireless energy transferring. With the consideration of the mobility of wireless devices and the diversity of charging attenuation in the edge computing environment, the device shall make the right charging decision at runtime during the movement to maximize the long-term average utility. In this article, we discover that this problem can be described as a Markov Decision Process (MDP) and accordingly design an MDP based wireless energy harvesting strategy. Based on extensive simulation-based experimental results, we observe that the proposed strategy outperforms several existing schemes and significantly improves the system utility.
Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match

In this paper, we develop \scg~(\text{SCG}{$++$}), the first efficient variant of a conditional gradient method for maximizing a continuous submodular function subject to a convex constraint. Concretely, for a monotone and continuous DR-submodular function, \SCGPP achieves a tight $[(1-1/e)\OPT -\epsilon]$ solution while using $O(1/\epsilon^2)$ stochastic gradients and $O(1/\epsilon)$ calls to the linear optimization oracle. The best previously known algorithms either achieve a suboptimal $[(1/2)\OPT -\epsilon]$ solution with $O(1/\epsilon^2)$ stochastic gradients or the tight $[(1-1/e)\OPT -\epsilon]$ solution with suboptimal $O(1/\epsilon^3)$ stochastic gradients. We further provide an information-theoretic lower bound to showcase the necessity of $\OM({1}/{\epsilon^2})$ stochastic oracle queries in order to achieve $[(1-1/e)\OPT -\epsilon]$ for monotone and DR-submodular functions. This result shows that our proposed \SCGPP enjoys optimality in terms of both approximation guarantee, i.e., $(1-1/e)$ approximation factor, and stochastic gradient evaluations, i.e., $O(1/\epsilon^2)$ calls to the stochastic oracle. By using stochastic continuous optimization as an interface, we also show that it is possible to obtain the $[(1-1/e)\OPT-\epsilon]$ tight approximation guarantee for maximizing a monotone but stochastic submodular set function subject to a general matroid constraint after at most $\mathcal{O}(n^2/\epsilon^2)$ calls to the stochastic function value, where $n$ is the number of elements in the ground set.
Modelling and simulation applications on cyber-physical systems' security and resilience

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A study on the drug prescribing pattern and use of antimicrobial agents at a tertiary care teaching hospital in eastern Nepal

Objective: To find out the prescribing practices in the B.P.Koirala Institute of Health Sciences with special emphasis on the utilization pattern of antimicrobial agents (AMA) and to compare with the drug prescribing practice of physicians in developed countries. Methods: A prospective cross sectional study was conducted in the month of October 1996. Prescriptions were collected from all the 94 in-patient case records and usingWHO basic drug indicators, the prescribing pattern was analysed. Results: Prescription analysis showed that the bed occupancy rate, out of 200 beds, was 47%. The average number of drugs per prescription was 5.26. 84% of all prescriptions contained AMA. It was the most commonly prescribed (42.8%) group of drugs followed by anti-inflammatory and analgesics (13.1%). Cloxacillin was the most commonly prescribed AMA followed by ampicillin. Ciprofloxacin plus metronidazole combination was the most frequently used. Prophylactic use of AMA was more in surgical departments. Therapeutic and prophylactic uses of AMA were appropriate only in 30.2% and 55.6% of cases respectively. The use of injections was frequent as evidenced by a mean of 2.12 injections per prescription. The duration of treatment was not mentioned for 45.6% of all the drugs prescribed. Conclusion: The results indicate that there is a scope for improving prescribing habits and minimizing the use of AMA. This could be facilitated by periodic prescribers education.
Semi-Supervised Deep Vision-Based Localization Using Temporal Correlation Between Consecutive Frames

Vision-based localization is a temporal informative task in which we can obtain information about the ego-motion of a vehicle from the historical information via examining consecutive frames. Sufficient temporal information helps to reduce the search space of the next location. Hence, both efficiency and accuracy of the localization system can be enhanced. This paper presents a semi-supervised deep vision-based localization algorithm, using a novel tubing strategy to find the starting location of a vehicle. We group different number of consecutive frames as sets of tubes based on their temporal correlation to achieve pair searching with variable tube sizes. We also enhance an off-the-shelf network model with our modified training data generation method to improve the discrimination power of the features given by the model. Experimental results show that our proposed temporal correlation based initialization module can confidently localize the starting location of a vehicle (for a certain journey), and achieve 40% precision improvement over that of the conventional CNN approaches.
A hybrid approach for generating reputation based on opinions fusion and sentiment analysis

ABSTRACT Amazon, eBay, IMDb as well as several websites provide a convenient platform where users share their opinions on any entities without hindrance. Though those opinions are too many to be examined one by one, this is why a general reputation value will help people make a decision toward a target entity (purchase, download, rent …). This fact makes reputation generation task very challenging because an inaccurate reputation system will directly damage the credibility and popularity of the target entity. This paper aims to improve a recent work that handles the task of generating reputation based on fuzing and mining opinions expressed in natural languages and user feedback ratings. Therefore, we have proposed a hybrid approach that, (i) separates reviews into positive and negative based on their sentiment polarity by applying the two classifiers Naïve Bayes and Linear Support Vector Machine (LSVM), (ii) groups positive and negative reviews into principal opinion sets based on their semantic relations, (iii) calculates a custom reputation value separately for positive and negative groups by considering some statistics of principal opinion sets and finally (iv) computes the final reputation value using Weighted Arithmetic Mean. Experimental results show a significant improvement with respect to recent work.
Integration of Back Calculation Anti Windup and Smith Predictor on PID Controller for Varying Time Delay System with Input Constraint

Proportional Integral Derivative (PID) controller is still the most widely used controller in the industry. However, the presence of varying time delay and input constraint cause PID controller performance to deteriorate and produce unsatisfactory operation. This situation becomes worsening if the varying time delay and input constraint come simultaneously during system operation. To tackle this issue, this paper focuses on the integration of smith predictor and back calculation anti windup on PID controller (PID-BC-SP) towards improving the performance of PID controller due to simultaneous presence of uncertain time delay and input constraint on the system. The designated controller is tested on a first order plus dead time model of glycerin bleaching process plant in order to observe the performance of the controller. Results obtained show that PID-BC-SP produces a better performance and higher robustness as compared to PID, PID to anti windup and PID with smith predictor controllers, whilst handling simultaneous presence of input constraint and varying time delay.
Evaluation of Physical Finger Input Properties for Precise Target Selection

The multitouch tabletop display provides a collaborative workspace for multiple users around a table. Users can perform direct and natural multitouch interaction to select target elements using their bare fingers. However, physical size of fingertip varies from one person to another which generally introduces a fat finger problem. Consequently, it creates the imprecise selection of small size target elements during direct multitouch input. In this respect, an attempt is made to evaluate the physical finger input properties i.e. contact area and shape in the context of imprecise selection.
Educational complex systems and open, flexible, and distance learning: a complexity theoretical perspective

ABSTRACT This reflection considers issues for researchers who study open, flexible, and distance learning (OFDL) systems. In particular, human learning is regarded as an emergent phenomenon occurring in educational complex systems, which may be non-digital or digital in nature. It is suggested the term learning in OFDL, such as learning analytics and machine learning, refers to computational techniques that help identify patterns in large datasets generated in OFDL systems. It is also proposed that OFDL systems are examples of complex systems, and an overview is provided of a set of relevant complexity conceptual principles. Issues are considered about types of learning data in OFDL systems and their lack of suitability for assessing higher-level explanatory and procedural understandings. Finally, an example is provided to illustrate how complexity ideas may be used with LA techniques to study an OFDL research topic. Implications of complexity theorizing for OFDL research are also discussed.
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization

Communication overhead is one of the key challenges that hinders the scalability of distributed optimization algorithms. In this paper, we study local distributed SGD, where data is partitioned among computation nodes, and the computation nodes perform local updates with periodically exchanging the model among the workers to perform averaging. While local SGD is empirically shown to provide promising results, a theoretical understanding of its performance remains open. In this paper, we strengthen convergence analysis for local SGD, and show that local SGD can be far less expensive and applied far more generally than current theory suggests. Specifically, we show that for loss functions that satisfy the Polyak-Kojasiewicz condition, $O((pT)^{1/3})$ rounds of communication suffice to achieve a linear speed up, that is, an error of $O(1/pT)$, where $T$ is the total number of model updates at each worker. This is in contrast with previous work which required higher number of communication rounds, as well as was limited to strongly convex loss functions, for a similar asymptotic performance. We also develop an adaptive synchronization scheme that provides a general condition for linear speed up. Finally, we validate the theory with experimental results, running over AWS EC2 clouds and an internal GPUs cluster.
Comprehensive assessment of 12 soft computing approaches for modelling reference evapotranspiration in humid locations

Owing to the lack of lysimetric data in many regions, the standard Penman–Monteith equation‐adopted by Food and Agriculture Organization of the United Nations (FAO56‐PM model) is usually used to calculate the reference evapotranspiration (ETo). However, as this model needs many meteorological parameters that cannot be easily obtained in many regions, other simple models along with the soft computing models are used to obtain ETo values. The paper presents a comprehensive comparison of 12 soft computing models: gene expression programming (GEP), neuro‐fuzzy with grid partitioning (NF‐GP), neuro‐fuzzy with sub‐clustering (NF‐SC), multivariate adaptive regression spline (MARS), boosted regression tree (BT), random forest (RF), model tree (MT), support vector machine (SVM), SVM‐firefly algorithm (SVM‐FA), extreme learning machine (ELM), neural network‐particle swarm optimization (NN‐PSO) and neural network‐differential evolution (NN‐DE), to estimate daily ETo values in humid regions. Therefore, daily meteorological data from two weather stations (during a 12 year period) were used to assess the models. The obtained results revealed that very good efficiency was obtained from all the applied methods. The temperature‐based SVM‐FA (root mean square error (RMSE) = 0.324 mm, Nash–Sutcliffe co‐efficient (NS) = 0.960) and NF‐GP (RMSE = 0.272 mm, NS = 0.974) models generally provided the best accuracy when estimating the ETo of Sari and Bablosar, two humid stations in northern Iran, respectively. The accuracy ranks of the other models (from best to worst) were found to be: NN‐PSO, NF‐SC, ELM, NN‐DE, MARS, GEP, RF, SVM, BT and MT. Among the radiation‐based models, the NF‐GP provided the best accuracy at estimating the ETo of both stations. The other models were ranked as: ELM, SVM‐FA, NN‐DE, NN‐PSO, MARS, RF, BT, NF‐SC, SVM and MT, respectively.
Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment

Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. An effective solution for cross-lingual entity alignment is crucial for many cross-lingual AI and NLP applications. Recently many embedding-based approaches were proposed for cross-lingual entity alignment. However, almost all of them are based on TransE or its variants, which have been demonstrated by many studies to be unsuitable for encoding multi-mapping relations such as 1-N, N-1 and N-N relations, thus these methods obtain low alignment precision. To solve this issue, we propose a new embedding-based framework. Through defining dot product-based functions over embeddings, our model can better capture the semantics of both 1-1 and multi-mapping relations. We calibrate embeddings of different KGs via a small set of pre-aligned seeds. We also propose a weighted negative sampling strategy to generate valuable negative samples during training and we regard prediction as a bidirectional problem in the end. Experimental results (especially with the metric Hits@1) on real-world multilingual datasets show that our approach significantly outperforms many other embedding-based approaches with state-of-the-art performance.
Machine Learning Methods in Classification of Text by Sentiment Analysis of Social Networks

In recent years, we became witnesses of a large number of websites that enable users to contribute, modify, and grade the content. Users have an opportunity to express their personal opinion about specific topics. The examples of such web sites include blogs, forums, product review sites, and social networks. Micro-blogs are a challenging new source of information for data mining techniques. It allows users to publish brief message updates, which can be submitted in many different channels, including the Web and text messaging service. One of the most notable micro-blogging services is Twitter, Face Book Etc.,. Twitter messages are short, and generated constantly, and well suited for knowledge discovery. This paper presents an empirical study of efficiency of machine learning techniques in classifying text messages by sentiment Analysis. Keywords: Blogs, Machine Learning, Sentiment Analysis.
Patient Perceptions of Patient-Empowering Nurse Behaviors, Patient Activation, and Functional Health Status After Surgery

PATIENT PERCEPTIONS OF PATIENT-EMPOWERING NURSE BEHAVIORS, PATIENT ACTIVATION, AND FUNCTIONAL HEALTH STATUS AFTER SURGERY Teresa A. Jerofke, BSN, MSN, APNP-BC Marquette University, 2013 Patient empowerment has been advocated as a way to engage patients in selfmanagement of chronic illnesses in emerging patient-centered models for healthcare improvement. The majority of research on patient empowerment has studied empowerment as an outcome in outpatient settings, with little attention to provider processes used to empower patients during a hospitalization. Post-operative patients with life-threatening chronic illnesses face multiple illness-related transitions associated with the recovery from their surgery and taking on the role of managing their life-threatening chronic illnesses upon hospital discharge. A correlational, longitudinal design framed by Meleis’ Transitions Theory and the Individual and Family Self-Management Theory (IFSMT) was used to determine the relationship between patient perceptions of patient-empowering nurse behaviors in an acute care setting and patient activation and functional health status six-weeks postdischarge in patients who recently underwent a surgical procedure for cancer or cardiac disease. In addition, tests of validity and reliability were conducted on a newly constructed instrument, the Patient Perceptions of Patient-Empowering Nurse Behaviors Scale (PPPNBS). One hundred thirteen post-surgical cancer and cardiac patients participated. Patient perceptions of patient-empowering nurse behaviors and baseline patient activation were measured prior to discharge with the PPPNBS and 13-item Patient Activation Measure (PAM 13). Patient activation and functional health status were measured sixweeks following discharge with the PAM 13 and SF-36. Multiple linear regression using a simultaneous equation approach was used to identify significant relationships. Patient perceptions of patient-empowering nurse behaviors were significantly positively associated with post-discharge patient activation, which was significantly positively associated with mental functional health status. Length of stay was the only significant predictor of physical functional health status. The PPPNBS demonstrated acceptable validity and reliability in post-surgical patients with a life-threatening chronic illness. Implications for nursing practice, nursing research, and nursing education are identified. Patient-empowering nurse behaviors can be used to facilitate engagement in self-management behavior, improve functional health status, and should be examined as a way to improve the cost of chronic illness care through improved patient activation levels. Transitions Theory and the IFSMT provided a useful framework to examine the contribution of nursing care, represented by patient-empowering nurse behaviors, to patient self-management outcomes.
Validation de la version française du H.A.D.S. dans une population de patients cancéreux hospitalisés

The psychological morbidity in hospitalized cancer patients is considerable and there is an increasing need for developing valid instruments to assess anxiety and depression in oncology. The aim of this study was to test the validity of the French version of the Hospital Anxiety and Depression Scale (HADS) in a sample of 228 hospitalized cancer patients
An Intrusion Detection Method for Industrial Control System Based on Gate Recurrent Unit

We proposed an industrial control system intrusion detection method based on gate recurrent unit neural network to handle the problem that the intrusion detection method based on traditional machine learning algorithm such as SVM, decision tree, NN etc., cannot effectively deal with massive, high-dimensional, time related network traffic data in industrial control system. We used the update gate and the reset gate of GRU to save the information of the data in the time dimension. Its deep structure can fully learn the data features, and we used Adam algorithm to optimize the gradient training process of the neural network. Comparison experiments were conducted with the intrusion detection method based on machine learning algorithms such as SVM, decision tree, NN, RNN, LSTM etc. The results show that the proposed method has higher classification accuracy than SVM, decision tree, NN and RNN, and the accuracy is basically the same as LSTM but the training time is greatly reduced.
Sexual Assault and Sexual Harassment in the U.S. Military: Volume 3. Estimates for Coast Guard Service Members from the 2014 RAND Military Workplace Study

Abstract : The Department of Defense (DoD) has assessed service member experiences with sexual assault and harassment since at least 1996, when Public Law 104-201 first required a survey of the gender relations climate experienced by active-component forces. Since 2002, four Workplace and Gender Relations Surveys, as they are known in 10 U.S.C. 481, have been conducted with active-component forces (in 2002, 2006, 2010, and 2012). DoD conducted reserve-component versions of this survey in 2004, 2008, and 2012. The results of the 2012 survey suggested that more than 26,000 service members in the active component had experienced unwanted sexual contacts in the prior year, an estimate that received widespread public attention and concern. In press reports and congressional inquiries, questions were raised about the validity of the estimate, about what unwanted sexual contact included, and about whether the survey had been conducted properly. Because of these questions, some members of Congress urged DoD to seek an independent assessment of the number of service members who experienced sexual assault or sexual harassment. The Sexual Assault Prevention and Response Office within the Office of the Secretary of Defense selected the RAND Corporation to provide a new and independent evaluation of sexual assault, sexual harassment, and gender discrimination across the military. As such, DoD asked the RAND research team to redesign the approach used in previous DoD surveys, if changes would improve the accuracy and validity of the survey results for estimating the prevalence of sexual crimes and violations. In the summer of 2014, RAND fielded a new survey as part of the RAND Military Workplace Study. This report, Volume 3 in our series, presents survey results for the U.S. Coast Guard and the Coast Guard Reserve.
Music therapy in the treatment of children with migraine

A self-contained single-use apparatus for cleaning and hydrating a pair of contact lenses comprises a housing including a pair of hollow snap-lock lens containers in which opposed layers of a deformable relatively hydrophobic reactive material having surfaces arranged for non-abrasive contiguous wetted contact with the optical surfaces of the lenses when the lenses are enclosed in the housings. While in contact with the optical surfaces contaminants in the lenses are attracted to the contacting surfaces of the reactive layers. Upon removal of the lenses the attracted contaminants remain on the contacting surfaces and are disposed of with the housing. The container contains sufficient absorbed liquid for maintaining the lens in a hydrated condition while being treated in the container during the treatment process.
University of Indonesia Participation at WEBIR-CLEF 2005

We present a report on our participation in the English-Indonesian image ad- hoc task of the 2005 Cross-Language Evaluation Forum (CLEF). We chose to translate the Indonesian query set into English using a commercial machine translation tool called Transtool. We show that some improvement in retrieval effectiveness can be obtained using a query expansion technique. We used an approach that combines the retrieval results of the query on text and on image.