nils-herrmann/clean_scirepeval huggingface dataset:
Making Life Better: A whole system strategic framework for public health

Monitoring Health Wellbeing across the population Morning Parallel Session 1 Chair Malachy Bradley, Assistant Director/Senior Planner at Eastern and Midland Regional Assembly The Knowledge 4 Health Conference 2016 took place on 25th May at the Royal Hospital Kilmainham, in Dublin. This all Island Conference explored innovative and practical ways of providing knowledge support for the implementation of two public health frameworks. These are 'Healthy Ireland: A Framework for Improved Health Wellbeing 2013 2025' in the Republic of Ireland and 'Making Life Better A Whole System Strategic Framework for Public Health 2013 2023' in Northern Ireland. 'Innovation' was the cross cutting theme of the Conference which included a broad range of speakers. It was a very interactive programme that ended with a key note speaker and an expert panel discussion answering questions from the audience. The Conference included two plenary and four parallel sessions focusing on the following themes: Monitoring Health and Wellbeing Across the Population Crossing Boundaries for Better Knowledge Knowledge Exchange: Formulating Policy and Practice Information Systems for Health and Wellbeing There was an opportunity for interested organisations to display any projects (including online tools and systems) that support knowledge for health on the Island during an interactive showcase over lunch. There was also a photographic exhibition titled "Health and Wellbeing" exploring the many meanings of health and wellbeing as reflected in the two public health frameworks on the Island. To View the presentation, Click here To listen to the presentation, Click here To view photos from the event, Click here To view the programme, Click here. To view speaker profiles, Click here.
Robust Multivariable Feedback Control of Natural Gas Diesel RCCI Combustion

Advanced combustion concepts such as Reactivity Controlled Compression Ignition (RCCI) demonstrate very high thermal efficiencies combined with ultra low NOx emissions. As RCCI is sensitive for operating conditions, closed loop control is a crucial enabler for stable and robust combustion. The feedback design is complex due to the coupling between inputs, such as intake manifold temperature, blend ratio (BR) and air to fuel ratio (l) and, on the other hand, combustion parameters. Contrary to earlier, parallel SISO approaches, a multivariable feedback control strategy is applied, which uses a new combination of control parameters. Following the linearisation of a validated multi zone RCCI combustion model, a MIMO feedback control strategy is designed. This cycle to cycle control strategy uses diesel injection timing, diesel fuel quantity, and natural gas (NG) fuel quantity information to track the desired engine load, ignition delay, and a new control parameter: BR. The designed controller is validated with simulation cases for disturbance rejection and simultaneous multivariable reference tracking. Simulation results demonstrate robust performance, effective decoupling action, and fast settling time (within 3 engine cycles) of the designed controller.
An Infrared Small Target Detecting Algorithm Based on Human Visual System
Venue: IEEE Geoscience and Remote Sensing Letters

Infrared (IR) small target detection with high detection rate, low false alarm rate, and multiscale detection ability is a challenging task since raw IR images usually have low contrast and complex background. In recent years, robust human visual system (HVS) properties have been introduced into the IR small target detection field. However, existing algorithms based on HVS, such as difference of Gaussians (DoG) filters, are sensitive to not only real small targets but also background edges, which results in a high false alarm rate. In this letter, the difference of Gabor (DoGb) filters is proposed and improved (IDoGb) which is an extension of DoG but is sensitive to orientations and can better suppress the complex background edges, then achieves a lower false alarm rate. In addition, multiscale detection can be also achieved. Experimental results show that the IDoGb filter produces less false alarms at the same detection rate, while consuming only about 0.1 s for a single frame.
A Small Target Detection Method based on Human Visual System and Confidence Measurement
Venue: J. Inf. Hiding Multim. Signal Process.

Robust small target detection of low signal to noise ratio (SNR) is very important in infrared search and track applications.Due to the complex background, current algorithms have some unsolved issues about small target detection capabilities, such as detection rate, false alarm rate and speed. They generally improve one or two of the aforementioned detection capabilities while sacrificing the others. In order to pursue good performance in detection rate, false alarm rate and speed simultaneously, an infrared small target detection algorithm based on mechanism of visual attention and two layers structure was proposed.Firstly, we detected salient regions that may contain targets by morphological Top hat and threshold operation. Then target recognition was performed by confidence measurement based on histogram in the salient regions. Experimental results show the proposed algorithm has ideal robustness and efficiency for real infrared small target detection applications.
An Efficient Infrared Small Target Detection Method Based on Visual Contrast Mechanism
Venue: IEEE Geoscience and Remote Sensing Letters

Robust and efficient detection of an infrared (IR) small target is very important in the IR search and track system. Based on the contrast mechanism of the human visual system, an IR small target detection method with high detection rate, low false alarm rate, and short processing time is proposed in this letter. This method consists of two stages. At the first stage, with the top hat filter and an adaptive threshold operation based on the constant false alarm rate applied to the original image, the suspicious target regions are obtained. In this way, the computing time of the following steps would be reduced a lot; meanwhile, the desired and predictable detection probability with the constant false alarm probability is maintained. At the second stage, we first define a new efficient local contrast measure between the target and the background, and the local self similarity of an image is introduced to calculate the local saliency map. With the combination of the local self similarity and local contrast, an efficient saliency map is obtained, which cannot only increase the signal to clutter ratio but also suppress residual clutter simultaneously. Then, a simple threshold operation on the saliency map is used to get the true targets. Experimental results indicate that the proposed method is superior in detection rate, false alarm rate, and processing time compared with the contrast algorithms, and it is an efficient method for IR small target detection in a complex background.
We zijn begonnen! Tweede meting monitor Vakmanschap en technologieroutes

Dit rapport schetst een beeld van experimenten met doorlopende leerlijnen in het vmbo en mbo die sinds het schooljaar 2014 2015 zijn gestart. Het beeld is nog te jong en te divers om algemeen geldende conclusies te trekken. Toch zijn een aantal aanknopingspunten te destilleren voor het verdere onderzoek, het beleid en/of de implementatie door scholen.
A Stackelberg Game for DSTC Based Cognitive Radio Networks with Multiple Cooperative Relays
Venue: 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS)

The spectrum utility can be improved when cooperative communication technology is incorporated into cognitive radio network, and both the licensed users and secondary users can benefit from cooperating. In this cooperative cognitive radio network, a primary user and multiple secondary users cooperate with each other adopting space time coding relay scheme to benefit win win cooperation. Considering the primary user owns the spectrum and both the primary user and secondary users are selfish and reasonable, this cooperative cognitive network is formulated as a Stackelberg game. Based on utility maximizing, this paper studies the spectrum allocation and relay selection problem. A spectrum allocation algorithm is derived under given number of secondary users, and a low complexity relay selection strategy is proposed to maximize the utility of the primary user while guaranteeing the data rate of the primary user and the fairness among the secondary users. Numerical results show the feasibility and validity of the proposed relay selection scheme.
Cooperative Game and Relay Pairing in Cognitive Radio Networks
Venue: CrownCom

In this paper, we focus on cooperative spectrum access in a cognitive radio networks (CRN) where secondary users (SUs) serve as relays for primary users (PUs) to improve their throughput, and in return SUs can gain transmission opportunities. To optimize the overall utility of a cooperative CRN, we first investigate the cooperation between a single pair of PU and SU with Stackelberg game model, where PU determines access time allocation while SU determines relaying power for the PU. Based on the analytical results, cooperation pairing between multiple PUs and SUs is modeled as a bipartite matching problem and solved using Gale Shapley algorithm. Numerical results demonstrate that, with the proposed schemes, overall utility for PUs and SUs can be balanced with low computational complexity.
Bit Interleaved Coded Modulation (BICM) for ATSC 3.0
Venue: IEEE Transactions on Broadcasting

In this paper, we summarize and expound upon the choices made for the bit interleaved coded modulation (BICM) part of the next generation terrestrial broadcast standard known as ATSC 3.0. The structure of the ATSC 3.0 BICM consists of a forward error correcting code, bit interleaver, and constellation mapper. In order to achieve high efficiency over a wide range of reception conditions and carrier to noise (C/N) ratio values, several notable new elements have been standardized. First, 24 original low density parity check (LDPC) codes have been designed, with coding rates from 2/15 (0.13) up to 13/15 (0.87) for two code sizes: 16 200 bits and 64 800 bits. Two different LDPC structures have been adopted; one structure more suited to medium and high coding rates and another structure suited to very low coding rates. Second, in addition to quaternary phase shift keying, non uniform constellations (NUCs) have been chosen for constellation sizes from 16QAM to 4096QAM to bridge the gap to the Shannon theoretical limit. Two different types of NUCs have been proposed: 1 D NUCs for 1024 and 4096 point constellations, and 2 D NUCs for 16 64 and 256 point constellations. 2 D NUCs achieve a better performance than 1 D NUCs but with a higher complexity since they cannot be separated into two independent I/Q components. NUCs have been optimized for each coding rate for the 64 800 bits LPDCs. The same constellations are used for 16 200 bits LDPCs, although they have been limited up to 256QAM. Finally, a bit interleaver, optimized for each NUC/coding rate combination, has been designed to maximize the performance. The result is a BICM that provides the largest operating range (more than 30 dB, with the most robust mode operating below 5 dB C/N) and the highest spectral efficiency compared to any digital terrestrial broadcast system today, outperforming the current state of the art DVB T2 standard BICM by as much as 1 dB in some cases. ATSC 3.0 will also provide a considerable increase in the maximum transmission capacity when using the high order NUCs such as 1024QAM and 4096QAM, which will represent a major milestone for terrestrial broadcasting since the highest order constellation currently available is uniform 256QAM. This paper describes the coding, modulation, and bit interleaving modules of the BICM block of ATSC 3.0 and compares its performance with other DTT standards such as ATSC A/53 and DVB T2.
Application of deep learning to computer vision: A comprehensive study
Venue: 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV)

Deep learning is a new era of machine learning research, where many layers of information processing stages are exploited for unsupervised feature learning. Using multiple levels of representation and abstraction, it helps a machine to understand about data (e.g. images, sound and text) more accurately. Many deep learning models have been proposed for solving the problem of different applications. Therefore, a comprehensive knowledge of these models is demanded to select the appropriate one for a specific application areas in signal or data processing. This paper reviews several deep learning models proposed for different application area in the field of computer vision, and makes a comprehensive evaluation of two well known models namely AlexNet and VGG_S in nine different benchmark datasets. The experimental results show that these two models perform better than the existing state of the art deep learning models in one dataset.
Using elliptical best fits to characterize dental shapes.
Venue: American journal of physical anthropology

A variety of geometric morphometric methods have recently been used to describe dental shape variation in human evolutionary studies. However, the applicability of these methods is limited when teeth are worn or are difficult to orient accurately. Here we show that elliptical best fits on outlines of dental tissues below the crown provide basic size and orientation free shape descriptors. Using the dm(2) and M(3) as examples, we demonstrate that these descriptors can be used for taxonomic purposes, such as distinguishing between Neanderthal and recent modern human teeth. We propose that this approach can be a useful alternative to existing methodology.
Comparative photographic evaluation of various geometric and mathematical proportions of maxillary anterior teeth: A clinical study
Venue: Indian journal of dental research official publication of Indian Society for Dental Research

This study aimed to investigate the existence of the golden proportion, recurring esthetic dental (RED) proportion and golden percentage between the frontal view widths of the maxillary anterior natural dentition among students of Indian origin by the aid of digital photography.  This study was conducted with 80 dental students (41 female and 39 male) with ages ranging from 20 to 23 years. Students whose natural smile did not develop any visual tension with regard to the study's and their own criteria were selected as having an esthetic smile. Photographs were taken, and the mesiodistal widths of six maxillary anterior teeth were measured digitally using software. Once the measurements were recorded three different theories of proportion were applied and statistical analysis was done.  The golden proportion, i.e. 62% RED proportion and golden percentage were not observed in the subjects. According to the subjects evaluated, the average width of the maxillary lateral incisor was 72% of the frontal view width of the central incisor. The average width of the canine was 84% of the frontal view width of the lateral incisor.  The golden proportion and RED proportion were not observed in the natural smiles of subjects who were deemed to have an esthetic smile. The values proposed for the golden percentage theory were not observed in subjects with an esthetic smile. Average frontal view percentage widths of the maxillary anterior dentition exist and can be useful in predicting naturally occurring widths in smiles deemed to be esthetic in a specific population.
Practical and low cost whole organism motility assay: A step by step protocol.
Venue: Molecular and cellular probes

Here, we provide a step by step protocol for a practical and low cost whole organism assay for the screening of chemical compounds for activity against parasitic worms. This assay has considerable advantages over conventional methods, mainly in relation to ease of use, throughput, time and cost. It is readily suited to the screening of hundreds to thousands of compounds for subsequent hit to lead optimisation, and should be applicable to many different parasites and other organisms commensurate with the size of wells in the microtiter plates used for phenotypic screening.
Cataract Drug Delivery Systems (Dropless vs. Nondropless Cataract Surgery)
Venue: International ophthalmology clinics

Cataract surgery is the most commonly performed procedure in the world, estimated at >20 to 25 million in number annually. With the aging population growing, the volume will continue to rise. Although there have been advancements in technology, techniques, and training to increase the safety and efficacy of cataract surgery, there has not been a commensurate evolution in the prophylaxis against infectious postoperative endophthalmitis (IPOE) and inflammation. Although the incidence of IPOE is small and ranges from 0.04% to 0.36% its devastating, sight threatening potential requires constant vigilance and motivation to further minimize its occurrence. In addition, postoperative cystoid macular edema (CME) persists as a significant cause of suboptimal visual recovery, which is particularly important given the expanding population of diabetic patients. More recently, there has been growing interest and acceptance of 'dropless' (eg, intracameral, transzonular, pars plana) alternatives to topical drops in the treatment of postoperative cataract patients. This interest has been enhanced by the development of a commercially available intracameral preparation of cefuroxime, as well as off label, compounded use of moxifloxacin with or without dexamethasone, in addition to compounded transzonular and/ or pars plana triamcinolone acetonide, moxifloxacin, and vancomycin. We will review the published literature on prophylaxis against IPOE and inflammation in cataract surgery, and discuss the different drug delivery methods.
A qualitative multi attribute model for the selection of the private hydropower plant investments in Turkey: By foundation of the search results clustering engine (Carrot2) hydropower plant clustering, DEXi and DEXiTree

The electricity demand in Turkey has been increasing for a while. Hydropower is one of the major electricity generation types to compensate this electricity demand in Turkey. Private investors (domestic and foreign) in the hydropower electricity generation sector have been looking for the most appropriate and satisfactory new private hydropower investment (PHPI) options and opportunities in Turkey. This study aims to present a qualitative multi attribute decision making (MADM) model, that is easy, straightforward, and fast for the selection of the most satisfactory reasonable PHPI options during the very early investment stages (data and information poorness on projects)  The data and information of the PHPI options was gathered from the official records on the official websites. A wide and deep literature review was conducted for the MADM models and for the hydropower industry. The attributes of the model were identified, selected, clustered and evaluated by the expert decision maker (EDM) opinion and by help of an open source search results clustering engine (Carrot 2 (helpful for also comprehension) The PHPI options were clustered according to their installed capacities main property to analyze the options in the most appropriate, decidable, informative, understandable and meaningful way. A simple clustering algorithm for the PHPI options was executed in the current study. A template model for the selection of the most satisfactory PHPI options was built in the DEXi (Decision EXpert for Education) and the DEXiTree software.  The basic attributes for the selection of the PHPI options were presented and afterwards the aggregate attributes were defined by the bottom up structuring for the early investment stages. The attributes were also analyzed by help of Carrot 2 The most satisfactory PHPI options in Turkey in the big options data set were selected for each PHPI options cluster by the EDM evaluations in the DEXi.  The recommended DEXi PHPI selection model by the search results clustering engine within a country wise case offered the possibility of easy, meaningful and satisfying continental or worldwide applications for the private investors and the international financial institutions such as the African Development Bank, or the World Bank was the main contribution.
A Dynamic Risk Based Access Control Model for Cloud Computing
Venue: 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud) Social Computing and Networking (SocialCom) Sustainable Computing and Communications (SustainCom) (BDCloud SocialCom SustainCom)

Cloud computing environment is full of massive resources and provides resource services to the outside environment. There are lots of resource access requests both from the dynamic internal environment of cloud and the uncontrollable external environment. Traditional access control method is being widely used in cloud computing, whose rule based method leads to huge security risk and can't fulfill the security needs of the cloud. In this paper, a dynamic risk based access control model (DRAC) is proposed, which emphasizes the risk measurement as an auxiliary decision indicator. Moreover, a dynamic threshold of the risk is derived from the history records, and the comprehensive final decision is affect by the policy, risk measurement and dynamic threshold. To improve performance, the sliding window calculation method based on data stream is adopted. Finally, we analyze the scalability and effectiveness of the model in cloud environment application.
A framework and risk assessment approaches for risk based access control in the cloud
Venue: J. Netw. Comput. Appl.

Cloud computing is advantageous for customers and service providers. However, it has specific security requirements that are not captured by traditional access control models, e.g. secure information sharing in dynamic and collaborative environments. Risk based access control models try to overcome these limitations, but while there are well known enforcement mechanisms for traditional access control, this is not the case for risk based policies. In this paper, we motivate the use of risk based access control in the cloud and present a framework for enforcing risk based policies that is based on an extension of XACML. We also instantiate this framework using a new ontology based risk assessment approach, as well as other models from related work, and present experimental results of the implementation of our work.