nils-herrmann/clean_scirepeval huggingface dataset:
Development Research Group The World Bank World Bank Institute Survey Tools for Assessing Service Delivery

_____________________ Contributions and comments of Magnus Lindelöw and Aminur Rahman are gratefully acknowledged. 2 Contents 1.  1 2. Public spending, service delivery, and outcomes 2 3. Public sector agencies 2 4.  Features of PETS and QSDS 5 A. Key features 5 B. Other survey-based tools 6 C. Uses and applications of PETS/QSDS 8 5. How to design and implement a PETS and QSDS 11 A. General 11 B. Fact-finding, consultations and purpose of the study 11 C. Linking PETS/QSDS with other surveys 17 L. Panel data 17 6. Sampling strategy and data availability 18 A. Unit of Study 18 B. Data availability 18 C. Private for-profit and not-for-profit 19 D. Sample frame, sample size, and stratification 19 E. Sampling issues involved in linking PETS and QSDS 20 F. Non-sampling error and field-testing 20 7. Findings from PETS 21 A. General 21 B. Leakage (capture) of public funds 21 C. Incentives to staff (moral hazard) 26 8. Findings from a QSDS 28 Findings and emerging issues 30 Bibliography 33
E-commerce business strategy analysis and inspiration: Taking Taobao as an example

Taobao is a network retailer which founded in May 2003 and now is the most popular online retail platform in China with nearly 500 million registered users. More than 60 million people visit Taobao everyday and over 48000 items are sold every minute on this platform. During the expansion progress, Taobao has transformed from a C2C network market into a worldwide E-commerce trading platform including C2C, group purchase, distribution and other electronic commerce modes. And its future strategy is focusing on community, content and local. This article studies service and business model of Taobao from five aspects: service description and market context, service supply chain, quality of service, service management system and risk management. An analysis of the present situation of Taobao reveals that it has formed its unique business pattern and raises problems and suggestions. For Taobao stepping into cross-border E-commerce, the article analyses its strength, weakness and points out the direction of its future.
Web credibility assessment: affecting factors and assessment techniques

In the current era of digital communication, users share information they consider important, using wikis, blogs and social networking Websites. The digital content includes valuable as well as biased, false and demagogic information. The objectives of this review paper are, i) To understand the perceptions of users regarding Web credibility judgment and the problems faced by them, ii) To review and list the factors used in various Web credibility judgment techniques, iii) To suggest a hybrid model that takes advantage of different credibility judgment techniques.  This paper adopted a systematic review methodology based on the guidelines of Kitchenham. Analysis. Over 100 papers were reviewed to compile the list of factors covered in the approaches. These analyses were summarized in the form of tables featuring the methods, types and categories of approaches as well as the factors covered.  Our findings show that by adopting more than one approach when assessing the credibility of a Web, measuring credibility assessment becomes easier. Therefore, a hybrid approach is presented for the conduct of credibility assessment using the different approaches available to measure accuracy, authority, aesthetics, professionalism, popularity, currency, impartiality and quality.  This paper hopes to contribute to the body of knowledge related to the identification of the factors (and categories they belong to) affecting Web credibility judgment and highlights the importance of a hybrid model for making accurate and effective Web credibility judgments.
Analysis of pattern, time and risk factors influencing recurrence in triple-negative breast cancer patients

The aim of the study was to assess the rate, pattern, and time of recurrence in patients with triple-negative breast cancer (TNBC) and to evaluate factors influencing recurrence and overall survival in this group of patients. Out of 2,534 consecutive breast cancer patients diagnosed between January 2005 and December 2006, 228 (9 %) were TNBC (ER/PR/HER2-negative). The clinicopathological characteristics were determined using descriptive statistics. The overall survival (OS) and disease-free survival (DFS) were calculated using the Kaplan-Meier method. The univariate and multivariate analyses were developed to identify factors influencing recurrence and survival in TNBC patients. After 6 years of observation, metastatic disease occurred in 35 % of all TNBC patients: 15 % in the brain, 14 % in the lungs, 11 % in the bones, 8 % in the liver, and 14 % had locoregional relapse. The highest risk of recurrence was during the first 3 years after primary treatment, and then, during the next 2 years of observation, it did not change. 6-year DFS and OS were 68 and 62 %, respectively. Factors influencing recurrence were tumor size and systemic adjuvant chemotherapy, while factors influencing overall survival were tumor size, nodal status, adjuvant/neoadjuvant treatment, and metastases in the brain, liver, and bones. Characteristic pattern of recurrence in time was revealed. The tumor size was responsible for recurrence despite lack of involvement of lymph nodes. Aggressive adjuvant/neoadjuvant treatment ordered in all clinical stages of TNBC (including N0) was factor responsible for avoiding local and distant relapse and prolonging overall survival.
Multiview Video Transmission Over Underwater Acoustic Path

Existing studies on multiview video streaming are proposed for terrestrial environments. They can be classified into request-reply and all camera transmission models, while both models suffer from a long switching delay and low video quality in an underwater acoustic path. The implementation of multiview video transmission in an underwater acoustic path requires reduction of the switching delay and the maintenance of high video quality. To this end, we propose a video transmission scheme, namely, Dolphin. Dolphin comprises time-shifted slot assignment, stochastic transmission, correlation-based compensation, and greedy transmission. Dolphin assigns asymmetric and time-shifted transmission slots to an encoder node and a user node according to a propagation delay. Stochastic transmission sends a potential camera's video that will be the most likely to be played back by the user before playback. If the stochastic transmission fails, the video frames of the user's desired camera are encoded with the mispredicted video frames to prevent switching delay from increasing. Moreover, greedy transmission sends all potential video frames, which are potentially displayed by a user, and determines rate allocation of the video frames based on the display probability. Evaluations using Mitsubishi Electric Research Laboratories' benchmark test sequences demonstrated that Dolphin achieved a short switching delay with a slight degradation in video quality irrespective of communication distance and acoustic path quality. For example, Dolphin decreases the switching delay by 92.7% compared with an existing request-reply model with 0.4-dB quality degradation at a communication distance of 300 m when a user tends to gaze at one camera.
Multi-sensor context-awareness for wearable computing

In this thesis we address the problem of perception, modeling, and use of context information from body-worn sensors for wearable computers. A context model is an abstraction of the user’s situation that is intelligible to the user, perceivable by sensors, and on the right level of abstraction, so that applications can use it to adapt their behavior. The issues of perception, modeling, and use of context information are thus strongly interrelated. Embedded in two application scenarios, we make contributions to the extraction of context from acceleration and audio sensors, the modeling of human interruptibility, and it’s estimation from acceleration, audio, and location sensors. We investigate the extraction of context from acceleration and audio data. We use body-worn acceleration sensors to classify the user’s physical activity. We developed a sensing platform which allows to record data from 12 three-dimensional acceleration sensors distributed over the body of the user. We classify activity of different complexity, such as sitting, walking, and writing on a white board, using a naïve Bayes’ classifier. We investigate which sensor placement on the body is best for recognizing such activities. We use auditory scene classification to extract context information about the social situation of the user. We classify the auditory scene of the user in street, restaurant, lecture, and conversation (plus a garbage class). We investigate which features are best suited for such a classification, and which feature selection mechanisms, sampling rates, and recognition windows are appropriate. The first application scenario is a meeting recorder that records not only audio and video of a meeting, but also additional, personal annotations from the user’s context. In this setting we make first contributions to the extraction of context information. We use acceleration sensors for simple activity recognition, and audio to identify different speakers in the recording and thus infer the flow of discussion and presentation. For the second application scenario, the estimation of the user’s interruptibility for automatic mediation of notifications, we developed a (context) model of human interruptibility. It distinguishes between the interruptibility of the user and that of the environment. We evaluate the model in a user study. We propose an algorithm to estimate the interruptibility within this model from sensor data. It combines low-level context information using so-called tendencies. A first version works on context from classifers trained in a supervised manner and uses hand-crafted tendencies. Although the algorithm produces good results with some 88-92% recognition score, it does not scale to large numbers of low-level contexts limiting the extensibility of the system. An improved version uses automatically found low-level contexts and learns the tendencies automatically. It thus allows to easily add new sensors and to adapt the system during run-time. We evaluated the algorithm on a data set of up to two days and obtained recognition scores of 90-97%.
Fuzzy cost-based FMECA for wind turbines considering condition monitoring systems

This paper discusses a fuzzy cost-based failure modes, effects, and criticality analysis (FMECA) approach for wind turbines. Conventional FMECA methods use a crisp risk priority number (RPN) as a measure of criticality which suffers from the difficulty of quantifying the risk. One method of increasing wind turbine reliability is to install a condition monitoring system (CMS). The RPN can be reduced with the help of a CMS because faults can be detected at an incipient level, and preventive maintenance can be scheduled. However, the cost of installing a CMS cannot be ignored. The fuzzy cost-based FMECA method proposed in this paper takes into consideration the cost of a CMS and the benefits it brings and provides a method for determining whether it is financially profitable to install a CMS. The analysis is carried out in MATLAB® which provides functions for fuzzy logic operation and defuzzification.
Tobacco and alcohol consumption among health sciences students in Cuba and Mexico.

Smoking and alcohol use (beyond social norms) by health sciences students are behaviors contradictory to the social function they will perform as health promoters in their eventual professions.



Identify prevalence of tobacco and alcohol use in health sciences students in Mexico and Cuba, in order to support educational interventions to promote healthy lifestyles and development of professional competencies to help reduce the harmful impact of these legal drugs in both countries.



A descriptive cross-sectional study was conducted using quantitative and qualitative techniques. Data were collected from health sciences students on a voluntary basis in both countries using the same anonymous self-administered questionnaire, followed by an in-depth interview.



Prevalence of tobacco use was 56.4% among Mexican students and 37% among Cuban. It was higher among men in both cases, but substantial levels were observed in women as well. The majority of both groups were regularly exposed to environmental tobacco smoke. Prevalence of alcohol use was 76.9% in Mexican students, among whom 44.4% were classified as at-risk users. Prevalence of alcohol use in Cuban students was 74.1%, with 3.7% classified as at risk.



The high prevalence of tobacco and alcohol use in these health sciences students is cause for concern, with consequences not only for their individual health, but also for their professional effectiveness in helping reduce these drugs' impact in both countries.
A Systematic Review of the Effects of Exercise and Physical Activity on Non-Specific Chronic Low Back Pain

Back pain is a major health issue in Western countries and 60%-80% of adults are likely to experience low back pain. This paper explores the impact of back pain on society and the role of physical activity for treatment of non-specific low back pain. A review of the literature was carried out using the databases SPORTDiscuss, Medline and Google Scholar. A general exercise programme that combines muscular strength, flexibility and aerobic fitness is beneficial for rehabilitation of non-specific chronic low back pain. Increasing core muscular strength can assist in supporting the lumbar spine. Improving the flexibility of the muscle-tendons and ligaments in the back increases the range of motion and assists with the patient's functional movement. Aerobic exercise increases the blood flow and nutrients to the soft tissues in the back, improving the healing process and reducing stiffness that can result in back pain.
Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations

Pretraining with expert demonstrations have been found useful in speeding up the training process of deep reinforcement learning algorithms since less online simulation data is required. Some people use supervised learning to speed up the process of feature learning, others pretrain the policies by imitating expert demonstrations. However, these methods are unstable and not suitable for actor-critic reinforcement learning algorithms. Also, some existing methods rely on the global optimum assumption, which is not true in most scenarios. In this paper, we employ expert demonstrations in a actor-critic reinforcement learning framework, and meanwhile ensure that the performance is not affected by the fact that expert demonstrations are not global optimal. We theoretically derive a method for computing policy gradients and value estimators with only expert demonstrations. Our method is theoretically plausible for actor-critic reinforcement learning algorithms that pretrains both policy and value functions. We apply our method to two of the typical actor-critic reinforcement learning algorithms, DDPG and ACER, and demonstrate with experiments that our method not only outperforms the RL algorithms without pretraining process, but also is more simulation efficient.
Mitochondrial dynamics: biology and therapy in lung cancer.

Lung cancer mortality rates remain at unacceptably high levels. Although mitochondrial dysfunction is a characteristic of most tumor types, mitochondrial dynamics are often overlooked. Altered rates of mitochondrial fission and fusion are observed in lung cancer and can influence metabolic function, proliferation and cell survival.



In this review, the authors outline the mechanisms of mitochondrial fission and fusion. They also identify key regulatory proteins and highlight the roles of fission and fusion in metabolism and other cellular functions (e.g., proliferation, apoptosis) with an emphasis on lung cancer and the interaction with known cancer biomarkers. They also examine the current therapeutic strategies reported as altering mitochondrial dynamics and review emerging mitochondria-targeted therapies.



Mitochondrial dynamics are an attractive target for therapeutic intervention in lung cancer. Mitochondrial dysfunction, despite its molecular heterogeneity, is a common abnormality of lung cancer. Targeting mitochondrial dynamics can alter mitochondrial metabolism, and many current therapies already non-specifically affect mitochondrial dynamics. A better understanding of mitochondrial dynamics and their interaction with currently identified cancer 'drivers' such as Kirsten-Rat Sarcoma Viral Oncogene homolog will lead to the development of novel therapeutics.
The Adoption of Cloud Computing by Irish SMEs – an Exploratory Study

Cloud Computing adoption has experienced a considerable rate of growth since its emergence in 2006. By 2011, it had become the top technology priority for organizations worldwide and according to some leading industry reports the cloud computing market is estimated to reach $241 billion by 2020. Reasons for adoption are multi-fold, including for example the expected realisation of benefits pertaining to cost reduction, improved scalability, improved resource utilization, worker mobility and collaboration, and business continuity, among others. Research into cloud computing adoption has to date primarily focused on the larger, multinational enterprises. However, one key area where cloud computing is expected to hold considerable promise is for the Small and Medium Sized Enterprise (SME). SMEs are recognized as being inherently different from their large firm counterparts, not least from a resource constraint perspective and for this reason, cloud computing is reported to offer significant benefits for SMEs through, for example, facilitating a reduction in the financial burden associated with new technology adoption. This paper reports findings from a recent exploratory study into Cloud Computing adoption among Irish SMEs. Despite its purported importance, this study found that almost half of the respondents had not migrated any services or processes to the cloud environment. Further, with respect to those who had transitioned to the cloud, the data suggests that many of these SMEs did not rigorously assess their readiness for adopting cloud computing technology or did not adopt in-depth approaches for managing their engagement with cloud. While the study is of an exploratory nature, nevertheless the findings have important implications for the development/ improvement of national strategies or policies to support the successful adoption of Cloud Computing technology among the SME market. This research has implications for academic research in this area as well as proposing a number of practical recommendations to support the SME cloud adoption journey.
Decoding mixed emotions from expression map of face images

In real life facial expressions show mixture of emotions. This paper proposes a novel expression descriptor based expression map that can efficiently represent pure, mixture and transition of facial expressions. The expression descriptor is the integration of optic flow and image gradient values and the descriptor value is accumulated in temporal scale. The expression map is realized using self-organizing map. We develop an objective scheme to find the percentage of different prototypical pure emotions (e.g., happiness, surprise, disgust etc.) that mix up to generate a real facial expression. Experimental results show that the expression map can be used as an effective classifier for facial expressions.
Communication Needs of Elderly at Risk of Falls and their Remote Family

The aging population experiences increased health risks, both physical and emotional. Two such risks are those of isolation and falling. This papers draws from HCI literature in these two independent research areas to explore the needs of family communication with elderly parents at risk of falls. We report on a study with 7 elderly parents and 3 of adult children, as well as a group interview with 12 elderly living in a sheltered accommodation. Findings indicate important emotional needs on both parts: adult children's anxiety for the wellbeing of their parents at risk of falls, and elderly's need for autonomy and their appreciation for an aesthetic design. We concluded with implications of these findings for designing for family communication in this challenging context.
Recent Advances in the Development of Cardiovascular Biomarkers.

Cardiovascular diseases (CVD) are initiated by endothelial dysfunction and resultant expression of adhesion molecules for inflammatory cells. Inflammatory cells secrete cytokines/chemokines and growth factors and promote CVD. Additionally, vascular cells themselves produce and secrete several factors, some of which can be useful for the early diagnosis and evaluation of disease severity of CVD. Among vascular cells, abundant vascular smooth muscle cells (VSMCs) secrete a variety of humoral factors that affect vascular functions in an autocrine/paracrine manner. Among these factors, we reported that CyPA (cyclophilin A) is secreted mainly from VSMCs in response to Rho-kinase activation and excessive reactive oxygen species (ROS). Additionally, extracellular CyPA augments ROS production, damages vascular functions, and promotes CVD. Importantly, a recent study in ATVB demonstrated that ambient air pollution increases serum levels of inflammatory cytokines. Moreover, Bell et al reported an association of air pollution exposure with high-density lipoprotein (HDL) cholesterol and particle number. In a large, multiethnic cohort study of men and women free of prevalent clinical CVD, they found that higher concentrations of PM2.5 over a 3-month time period was associated with lower HDL particle number, and higher annual concentrations of black carbon were associated with lower HDL cholesterol. Together with the authors’ previous work on biomarkers of oxidative stress, they provided evidence for potential pathways that may explain the link between air pollution exposure and acute cardiovascular events. The objective of this review is to highlight the novel research in the field of biomarkers for CVD.
Infrapatellar Fat Pad Stem Cells: From Developmental Biology to Cell Therapy

The ideal cell type to be used for cartilage therapy should possess a proven chondrogenic capacity, not cause donor-site morbidity, and should be readily expandable in culture without losing their phenotype. There are several cell sources being investigated to promote cartilage regeneration: mature articular chondrocytes, chondrocyte progenitors, and various stem cells. Most recently, stem cells isolated from joint tissue, such as chondrogenic stem/progenitors from cartilage itself, synovial fluid, synovial membrane, and infrapatellar fat pad (IFP) have gained great attention due to their increased chondrogenic capacity over the bone marrow and subcutaneous adipose-derived stem cells. In this review, we first describe the IFP anatomy and compare and contrast it with other adipose tissues, with a particular focus on the embryological and developmental aspects of the tissue. We then discuss the recent advances in IFP stem cells for regenerative medicine. We compare their properties with other stem cell types and discuss an ontogeny relationship with other joint cells and their role on in vivo cartilage repair. We conclude with a perspective for future clinical trials using IFP stem cells.
Error-tolerant graph matching using homeomorphism

Graph matching is the task of computing the resemblance of graphs. While in exact matching, a strict one-to-one correspondence should exist between two graphs or among their subgraphs, on the other hand in error-tolerant matching a strict correspondence is not necessary, and some similarity measure should be exhibited between two graphs or their subgraph depending on some tolerance value or noise. In this paper, we present a new approach to error-tolerant matching using graph homeomorphism to measure the structural resemblance of two graphs.