nils-herrmann/clean_scirepeval huggingface dataset:
Energy consumption, CO2 emissions, economic growth, and foreign trade relationship in Cyprus and Malta

This paper examines the long run and causal relationship issues between economic growth, carbon emissions, energy consumption, foreign trade ratio, and employment ratio in Cyprus and Malta by using autoregressive distributed lag (ARDL) bounds testing approach of cointegration and error correction based Granger causality models. Empirical results over the period 1980 2006 suggest an evidence of a long run relationship between the variables at 5% significance level only in Malta. Thus, Granger causality models are estimated only for Malta. Results for the existence and direction of Granger causality show that the causality runs from carbon emissions, energy consumption, foreign trade ratio, and employment ratio to economic growth but not vice versa in Malta. The overall results indicate that energy conservation policies, such as rationing energy consumption and controlling carbon dioxide emissions, are likely to have no adverse effect on the real output growth of Malta.
Label Embedding for Image Classification
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence

Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce. We propose to view attribute based image classification as a label embedding problem: each class is embedded in the space of attribute vectors. We introduce a function that measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct classes rank higher than the incorrect ones.  Label embedding enjoys a built in ability to leverage alternative sources of information instead of or in addition to attributes, such as, e.g. class hierarchies or textual descriptions. Moreover, label embedding encompasses the whole range of learning settings from zero shot learning to regular learning with a large number of labeled examples.
Optimal Signal Quality Index for Photoplethysmogram Signals
Venue: Bioengineering

A photoplethysmogram (PPG) is a noninvasive circulatory signal related to the pulsatile volume of blood in tissue and is typically collected by pulse oximeters. PPG signals collected via mobile devices are prone to artifacts that negatively impact measurement accuracy, which can lead to a significant number of misleading diagnoses. Given the rapidly increased use of mobile devices to collect PPG signals, developing an optimal signal quality index (SQI) is essential to classify the signal quality from these devices. Eight SQIs were developed and tested based on: perfusion, kurtosis, skewness, relative power, non stationarity, zero crossing, entropy, and the matching of systolic wave detectors. Two independent annotators annotated all PPG data (106 recordings, 60 s each) and a third expert conducted the adjudication of differences. The independent annotators labeled each PPG signal with one of the following labels: excellent, acceptable or unfit for diagnosis. All indices were compared using Mahalanobis distance, linear discriminant analysis, quadratic discriminant analysis, and support vector machine with leave one out cross validation. The skewness index outperformed the other seven indices in differentiating between excellent PPG and acceptable, acceptable combined with unfit, and unfit recordings, with overall F1 scores of 86.0% 87.2% and 79.1% respectively.
Applying quality index criterion for flexible multi detection of heartbeat using features of multimodal data
Venue: 2016 Computing in Cardiology Conference (CinC)

In many conditions contaminated signals and noises can distort electrocardiogram (ECG) signals, so synchronously measured signals could enhance analyzing the heart rate variability. Therefore, the algorithm should be able to identify reliable and optimal segment of these multimodal signals in order to accurately locate the heart beats.  This paper presents a flexible and multichannel feature extractor and classifier for the purpose of locate heartbeats by using the multi channel recording from PhysioNet Challenge 2014 database.  In this study, for feature extraction from multimodal data the common heartbeat detectors are used in other studies are employed, but the exhausted results are promising which indicates the importance and efficiency of the proposed fusion strategy and the decision of when fusion should be done as ECG itself is the best indicator of heartbeat detection. For fusion, after segmentation of waveforms, if ECG assumed to be noisy, an index is assigned to each signal based on periodicity of the extracted features and estimation of noises of detailed coefficients of discrete wavelet transform. The signal whose index is highest is processed in that segment.  The presented approach was evaluated by 200 recording of multimodal dataset provided for the PhysioNet/Computing in Cardiology Challenge 2014, and the average accuracy of the 95.39% was obtained.
Financing institutional long term care for the elderly in China: a policy evaluation of new models.
Venue: Health policy and planning

A rapid ageing population coupled with changes in family structure has brought about profound implications to social policy in China. Although the past decade has seen a steady increase in public funding to long term care (LTC) the narrow financing base and vast population have created significant unmet demand, calling for reforms in financing. This paper focuses on the financing of institutional LTC care by examining new models that have emerged from local policy experiments against two policy goals: equity and efficiency. Three emerging models are explored: Social Health Insurance (SHI) in Shanghai, LTC Nursing Insurance (LTCNI) in Qingdao and a means tested model in Nanjing. A focused systematic narrative review of academic and grey literature is conducted to identify and assess these models, supplemented with qualitative interviews with government officials from relevant departments, care home staff and service users. This paper argues that, although SHI appears to be a convenient solution to fund LTC, this model has led to systematic bias in affordable access among participants of different insurance schemes, and has created a powerful incentive for the over provision of unnecessary services. The means tested method has been remarkably constrained by narrow eligibility and insufficiency of funding resources. The LTCNI model is by far the most desirable policy option among the three studied here, but the narrow definition of eligibility has substantively excluded a large proportion of elders in need from access to care, which needs to be addressed in future reforms. This paper proposes three lines of LTC financing reforms for policy makers: (1) the establishment of a prepaid financing mechanism pooled specifically for LTC costs; (2) the incorporation of more stringent eligibility rules and needs assessment; and (3) reforming the dominant fee for service methods in paying LTC service providers.
Causal impact analysis for app releases in google play
Venue: SIGSOFT FSE

App developers would like to understand the impact of their own and their competitors' software releases. To address this we introduce Causal Impact Release Analysis for app stores, and our tool, CIRA, that implements this analysis. We mined 38,858 popular Google Play apps, over a period of 12 months. For these apps, we identified 26,339 releases for which there was adequate prior and posterior time series data to facilitate causal impact analysis. We found that 33% of these releases caused a statistically significant change in user ratings. We use our approach to reveal important characteristics that distinguish causal significance in Google Play. To explore the actionability of causal impact analysis, we elicited the opinions of app developers: 56 companies responded, 78% concurred with the causal assessment, of which 33% claimed that their company would consider changing its app release strategy as a result of our findings.
Mining and characterizing hybrid apps
Venue: WAMA@SIGSOFT FSE

Mobile apps have grown tremendously over the past few years. To capitalize on this growth and to attract more users, implementing the same mobile app for different platforms has become a common industry practice. Building the same app natively for each platform is resource intensive and time consuming since every platform has different environments, languages and APIs. Cross Platform Tools (CPTs) address this challenge by allowing developers to use a common code base to simultaneously create apps for multiple platforms. Apps created using these CPTs are called hybrid apps. We mine 15,512 hybrid apps and present the first study of its kind on such apps. We identify which CPTs these apps use and how users perceive them. Further, we compare the user perceived ratings of hybrid apps to native apps of the same category. Finally, we compare the user perceived ratings of the same hybrid app on the Android and iOS platforms.
Causal Impact for App Store Analysis
Venue: 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE C)

App developers naturally want to know which of their releases are successful and which are unsuccessful. Such information can help with release planning and requirements prioritisation and elicitation. To address this problem, I performed causal analysis on 52 weeks of popular app releases from Google Play and Windows Phone Store. The results reveal properties of successful releases in multiple app stores, and showcase causal analysis as a useful technique for developers seeking to know more about their software releases.
E Banking and Commercial Bank Performance in Nigeria: A Cointegration and Causality Approach

This work studied the relationship between electronic banking and the performance of Nigerian commercial banks. The study became necessary due to the increased adoption of the electronic banking which has redefined the banking service both in Nigeria and internationally. Electronic banking was proxied by value of Point of Sale transactions while commercial banking performance was proxied by customers' deposits. Engle Granger cointegration model was used to analyse data for the sample period January 2009 to December 2013. The results show that POS is not cointegrated with both the savings and time deposits but are cointegrated with demand deposits. It is recommended that the monetary authorities and commercial banks should embark on an all inclusive enlightenment campaign for the banking public on the benefits, convenience and importance of adopting e banking channels in completing their transactions.
Encouraging orthogonality between weight vectors in pretrained deep neural networks
Venue: Neurocomputing

Deep neural networks have recently shown impressive performance in several machine learning tasks. An important approach to training deep networks, useful especially when labeled data is scarce, relies on unsupervised pretraining of hidden layers followed by supervised finetuning. One of the most widely used approaches to unsupervised pretraining is to train each layer with the Contrastive Divergence (CD) algorithm. In this work we present a modification to CD with the goal of learning more diverse sets of features in hidden layers. In particular, we extend the CD learning rule to penalize cosines of the angles between weight vectors, which in turn encourages orthogonality between the learned features. We demonstrate experimentally that this extension to CD improves performance of pretrained deep networks on image recognition and document retrieval tasks.
Data security in mobile cloud computing paradigm: a survey, taxonomy and open research issues
Venue: The Journal of Supercomputing

The incessant spurt of research activities to augment capabilities of resource constrained mobile devices by leveraging heterogeneous cloud resources has created a new research impetus called mobile cloud computing. However, this rapid relocation to the cloud has fueled security and privacy concerns as users' data leave owner's protection sphere and enter the cloud. Significant efforts have been devoted by academia and research community to study and build secure frameworks in cloud environment, but there exists a research gap for comprehensive study of security frameworks in mobile cloud computing environment. Therefore, we aim to conduct a comprehensive survey to analyze various cryptographic, biometric and multifactor lightweight solutions for data security in mobile cloud. This survey highlights the current security issues in mobile cloud environment and infrastructure, investigates various data security frameworks and provides a taxonomy of the state of the art data security frameworks and deep insight into open research issues for ensuring security and privacy of data in mobile cloud computing platform.
In vitro tissue culture in breeding programs of leguminous pulses: use and current status
Venue: Plant Cell, Tissue and Organ Culture (PCTOC)

Legumes represent a vast family of plants including more than 600 genera and more than 13,000 species. Among them, the term "pulses" refers only to dried seed crops, excluding those grown mostly for oil extraction (like soybean) where dried peas, edible beans, lentils, chickpeas, cowpea, mungbean, blackgram and pigeonpea are the most common cultivated ones for human consumption due to their high nutritional value. They also have the ability of fixing nitrogen into the soil with symbiotic bacteria, which reduces the need for chemical fertilizers in crop rotations. Conventional breeding methods for pulses are laborious and time consuming before the release of new genotypes. Thus, alternative biotechnological approaches may be advantageous in this area. Tissue culture, plant regeneration strategies, gene transfer and plant transformation are studied in these pulses. Also, anther, microspore, embryo and ovary culture and their opportunity of application in these pulses are discussed.
Bacteria in the plant tissue culture environment
Venue: Plant Cell, Tissue and Organ Culture (PCTOC)

Bacteria and plants are joined in various symbiotic relationships that have developed over millennia and have influenced the evolution of both groups. Bacteria inhabit the surfaces of most plants and are also present inside many plant organs. These bacteria may have positive, neutral or negative impacts on their plant hosts. Probiotic effects may improve plant nutrition or increase resistance to biotic and abiotic stresses. Conversely pathogenic bacteria may kill or reduce the vigor of plant hosts. In addition some bacteria inhabit plants and profit from excess metabolites or shelter while not injuring the plant. Micropropagation of plants is based on the stimulation of organogenesis or embryogenesis from explants that are superficially decontaminated and placed into a sterile environment. If successful, this process removes bacteria from surfaces, but those inhabiting inner tissues and organs are usually not affected by these steriliants. In vitro conditions are designed for optimal plant growth and development, however these conditions are also often ideal for bacterial multiplication. The presence of bacteria in the in vitro environment was almost universally considered negative for plant culture, but more recently this view has been questioned. Certain bacteria appear to have a beneficial effect on the explants in culture; increasing multiplication and rooting, increasing explant quality, and organo and embryogenesis of recalcitrant genotypes. The most important role of beneficial bacteria for micropropagated plants is likely to be during acclimatization, when growth is resumed under natural conditions. This review includes the role of bacterial interactions in plants, especially those grown in vitro.
Extending Attack Graph Based Metrics for Enterprise Network Security Management

Measurement of enterprise network security is a long standing challenge to the research community. However, practical security metrics are vital for securing enterprise networks. With the constant change in the size and complexity of enterprise networks, and application portfolios as well, network attack surface keeps changing and hence monitoring of security performance is increasingly difficult and challenging problem. Existing attack graph based security metrics are inefficient in capturing change in the network attack surface. In this paper, we have explored the possible use of graph based distance metrics for capturing the change in the security level of dynamically evolving enterprise networks. We used classical graph similarity measures such as Maximum Common Subgraph (MCS) and Graph Edit Distance (GED) as an indicator of change in the enterprise network security. Our experimental results shows that graph similarity measures are efficient and capable of capturing changing network attack surface in dynamic (i.e. time varying) enterprise networks.
Evaluation of Network Risk Using Attack Graph Based Security Metrics
Venue: 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)

Network security management is a big challenge for network administrators due to increasing vulnerabilities. Vulnerabilities are the weakness of the network and allow malicious attackers access to resources controlled by an organization. To keep networks secure network administrators should be aware of all vulnerabilities through which an attacker can gain access. In this paper, we have considered the attack graph which describes how an attacker can compromise with the security of a network. To generate the attack graph, Multihost Multistage Vulnerability Analysis (MulVAL) tool is used. The generated graphs by this tool are logical attack graphs. These graphs are based on logical programming and based on dependencies among attack goal and configuration information. We have taken two security metrics, namely, exploitability metric and impact metric to analyze the risk associated with the network. Our preliminary results suggest that the size of the network has an impact on the vulnerability of a network.
Management and control technology for urban stormwater pollution

A number of proven and promising measures for managing and controlling water pollution caused by urban runoff and combined sewer overflows are re viewed in this paper to help guide planners and design ers in developing control programs suited to their local needs. This review summarizes a series of nationwide evaluations of urban stormwater management ap proaches, methodologies, and processes, sponsored by the U. S. Environmental Protection Agency (EPA).1"5 The evaluations include recent efforts to focus on most promising technology, and to identify receiving water, socioeconomic, and environmental impacts and benefits, in addition to assessments of the control measures' per formance, costs, and effectiveness. Urban stormwater problems are characterized by surface runoff events that are intermittent, unpredict able, and vary greatly with time, location, and in mag nitude. They are also characterized by a variety of pol lutants that may originate from a number of different types of sources. Control strategies, therefore, need to be specifically planned, evaluated, and designed to ac count for local characteristics. Clearly, they will differ markedly from measures used to control dry weather pollution. Wet weather control measures are classified into two broad categories: structural measures, and best man agement practices (BMPs) Structural measures are generally end of pipe facilities designed to control vol ume and pollution from stormwater and combined sewer overflows (CSOs) BMPs are nonstructural and low struct ral management alternatives for controlling urban ru off pollution; they include institutional and source control measures. The adoption and documentation of BMPs lags sig nificantly behind that of the structural alternatives for CSOs, in spite of the apparent greater cost effectiveness of BMPs. In part, this has resulted from the past em phasis of the EPA research and development program, which principally focused on controlling CSO problems in larger cities. Recent studies, however, indicate that pollution from urban runoff and other non point sources has caused major receiving water problems, including nutrient enrichment, introduction of toxic materials, turbidity, and sediment deposition.4,6"9 These problems arise with all types of development, both urban and rural. The magnitude of the urban runoff problem, therefore, may be comparable to the CSO problem, particularly whe floo ing and erosion impacts are con sidered. All costs are based on an Engineering News Record (ENR) Construction Cost Index of 3 000 (1913 100) approximating June 1979 dollars, for comparison pur poses.
JOINT TIMING SYNCHRONIZATION AND CHANNEL ESTIMATION USING PERFECT SEQUENCE IN UPLINK TIME DOMAIN SYNCHRONOUS OFDMA

Time Domain Synchronous Orthogonal Frequency Division Multiple Access (TDS OFDMA) is used in mobile broadband wireless access scheme in uplink transmission. This leads to multiple user interference due to timing offset and frequency offset. In this paper, the effect of timing offset and channel estimation in mobile broadband system is analysed. Time space two dimensional structure is used in TDS OFDMA and perfect sequence is used for guard interval to achieve perfect timing synchronization and channel estimation for each user. Simulations are performed for timing synchronization and channel estimation using perfect sequence under Urban channel, Indoor Office B channel and HIPER LAN A channel. Simulation results show that the timing synchronization is achieved and channel estimation performance using perfect sequence is better than CAZAC and PN Sequences.
Sport education model and self determination theory: an intervention in secondary school children

Using self determination theory as a guiding framework, this study analyzed the impact of the Sport Education Model in self determination and motivation, psychological basic need thwarting, enjoymentsatisfaction, boredom, and intention to be physically active in Physical Education (PE) of secondary school students in Spain. Two groups were selected for the study: an experimental group (EG; n=43) which received 19 volleyball lessons following the Sport Education Model, and a control group (CG; n=43) which received 19 traditional Physical Education lessons. Preand post intervention measures were taken in both groups. The results showed significant improvements in intrinsic motivation in EG. The results are discussed, and the suitability of the Sport Education Model to improve self determined behaviors in Physical Education is emphasized.
The Implementation of Character Education Model Based on Empowerment Theatre for Primary School Students

This study aimed at constructing character education model implemented in primary school. The research method was qualitative with five samples in total, comprising primary schools in Malang city/regency and one school as a pilot model. The pilot model was instructed by theatre coach teacher, parents, and school society. The result showed that hindrances were found during the implementation of character education in school, such a human resource problem, lack of facility, and less support from family. The implementation of character education in primary school has not yet attracted many parties and its evaluation form has not been developed sustainably. Reconstruction result and the implementation of character education based on empowerment theatre were likely to give new experience to students to develop better self characters. Observation and evaluation of student characters done by parents, teachers, schools, and communities might be used as the basis for policy making related to character education in schools, especially in primary school level.