nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows

The Vehicle Routing Problem with Time windows (VRPTW) is an extension of the capacity constrained Vehicle Routing Problem (VRP). The VRPTW is NP-Complete and instances with 100 customers or more are very hard to solve optimally. We represent the VRPTW as a multi-objective problem and present a genetic algorithm solution using the Pareto ranking technique. We use a direct interpretation of the VRPTW as a multi-objective problem, in which the two objective dimensions are number of vehicles and total cost (distance). An advantage of this approach is that it is unnecessary to derive weights for a weighted sum scoring formula. This prevents the introduction of solution bias towards either of the problem dimensions. We argue that the VRPTW is most naturally viewed as a multi-objective problem, in which both vehicles and cost are of equal value, depending on the needs of the user. A result of our research is that the multi-objective optimization genetic algorithm returns a set of solutions that fairly consider both of these dimensions. Our approach is quite effective, as it provides solutions competitive with the best known in the literature, as well as new solutions that are not biased toward the number of vehicles. A set of well-known benchmark data are used to compare the effectiveness of the proposed method for solving the VRPTW.
Analyzing User Activities, Demographics, Social Network Structure and User-Generated Content on Instagram

Instagram is a relatively new form of communication where users can instantly share their current status by taking pictures and tweaking them using filters. It has seen a rapid growth in the number of users as well as uploads since it was launched in October 2010. Inspite of the fact that it is the most popular photo sharing application, it has attracted relatively less attention from the web and social media research community. In this paper, we present a large-scale quantitative analysis on millions of users and pictures we crawled over 1 month from Instagram. Our analysis reveals several insights on Instagram which were never studied before: 1) its social network properties are quite different from other popular social media like Twitter and Flickr, 2) people typically post once a week, and 3) people like to share their locations with friends. To the best of our knowledge, this is the first in-depth analysis of user activities, demographics, social network structure and user-generated content on Instagram.
Real-time human pose recognition in parts from single depth images

We propose a new method to quickly and accurately predict human pose---the 3D positions of body joints---from a single depth image, without depending on information from preceding frames. Our approach is strongly rooted in current object recognition strategies. By designing an intermediate representation in terms of body parts, the difficult pose estimation problem is transformed into a simpler per-pixel classification problem, for which efficient machine learning techniques exist. By using computer graphics to synthesize a very large dataset of training image pairs, one can train a classifier that estimates body part labels from test images invariant to pose, body shape, clothing, and other irrelevances. Finally, we generate confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes.
 The system runs in under 5ms on the Xbox 360. Our evaluation shows high accuracy on both synthetic and real test sets, and investigates the effect of several training parameters. We achieve state-of-the-art accuracy in our comparison with related work and demonstrate improved generalization over exact whole-skeleton nearest neighbor matching.
15-PUBLIC / PRIVATE PARTNERSHIP IN HEALTH CARE SERVICES IN INDIA

It is widely accepted that the deficiencies in public sector health system can only be overcome by significant reforms. The need for reforms in India s health sector has been emphasized by successive plan documents since the Eighth Five-Year Plan in 1992, by the 2002 national health policy and by international donor agencies. The World Bank (2001:12,14), which has been catalytic in initiating health sector reforms in many states, categorically emphasized: now is the time to carry out radical experiments in India’s health sector, particularly since the status quo is leading to a dead end. . But it is evident that there is no single strategy that would be best option The proposed reforms are not cheap, but the cost of not reforming is even greater”.
Effects of nitrate toxicity in the Pacific white shrimp, Litopenaeus vannamei, reared with biofloc technology (BFT)

In cultures of the Litopenaeus vannamei with biofloc, the concentrations of nitrate rise during the culture period, which may cause a reduction in growth and mortality of the shrimps. Therefore, the aim of this study was to determine the effect of the concentration of nitrate on the growth and survival of shrimp in systems using bioflocs. The experiment consisted of four treatments with three replicates each: The concentrations of nitrate that were tested were 75 (control), 150, 300, and 600 mg NO3 −-N/L. To achieve levels above 75 mg NO3 −-N/L, different dosages of sodium nitrate (PA) were added. For this purpose, twelve experimental units with a useful volume of 45 L were stocked with 15 juvenile L. vannamei (1.30 ± 0.31 g), corresponding to a stocking density of 333 shrimps/m3, that were reared for an experimental period of 42 days. Regarding the water quality parameters measured throughout the study, no significant differences were detected (p > 0.05). Concerning zootechnical performance, a significant difference (p < 0.05) was verified with the 75 (control) and 150 treatments presenting the best performance indexes, while the 300 and 600 treatments led to significantly poorer results (p < 0.05). The histopathological damage was observed in the gills and hepatopancreas of the shrimps exposed to concentrations ≥300 mg NO3 −-N/L for 42 days, and poorer zootechnical performance and lower survival were observed in the shrimps reared at concentrations ≥300 mg NO3 −-N/L under a salinity of 23. The results obtained in this study show that concentrations of nitrate up to 177 mg/L are acceptable for the rearing of L. vannamei in systems with bioflocs, without renewal of water, at a salinity of 23.
A 7-µW 2.4-GHz wake-up receiver with -80 dBm sensitivity and high co-channel interferer tolerance

An ultra-low power wake-up receiver for 2.4-GHz wireless sensor networks, based on a fast sampling method, is presented. A novel multi-branch receiver architecture covers a wide range of interferer scenarios for highly occupied radio channels. The scalability of current consumption versus data rate at a constant sensitivity is another useful feature that fits a multitude of applications, requiring both short reaction times and ultra-low power consumption. The 2.4-GHz OOK receiver comprises a 3-branch analog superheterodyne front-end and six digital 31-bit correlating decoders. It is fabricated in a 130-nm CMOS technology. The current consumption is 2.9 μA at 2.5 V supply voltage and a reaction time of 30 ms. The receiver sensitivity is -80 dBm. Among other sub-100 μW state-of-the-art receivers, the presented implementation shows the best reported sensitivity.
ERP Acceptance: Organizational Change Perspective

ERP systems acceptance usually involves radical organizational change because it is often associated with fundamental organizational improvements that cut across functional and organizational boundaries. Recognizing that ERP systems involve organizational change and their implementation is overshadowed by a high failure rate, this study focuses attention on employees’ perceptions of such organizational change. For this purpose, the research incorporates a conceptual construct of attitude toward change that captures views about the need for organizational change. Structural equation analysis using LISREL provides significant support for the proposed relationships. Theoretical and practical implications are discussed along with limitations.
Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network

Early diagnosis, playing an important role in preventing progress and treating the Alzheimer’s disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposes to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans. Fully connected upper layers of the 3D-CNN are then finetuned for each task-specific AD classification. Experiments on the ADNI MRI dataset with no skull-stripping preprocessing have shown our 3D-CNN outperforms several conventional classifiers by accuracy and robustness. Abilities of the 3D-CNN to generalize the features learnt and adapt to other domains have been validated on the CADDementia dataset.
Radio-Over-Fiber Architectures

Future multigigabit wireless systems (MGWS) dedicated to home-area networking are emerging or are under development. They achieve high data rate up to 5-6 Gb/s over short range. To expand the radio coverage, we propose radio-over-fiber (RoF) architectures, from the most basic (point-to-point) to the most innovative (multipoint-to-multipoint). These RoF infrastructures combine radio and optical links. The simple way to implement RoF technology is to manage the optical media, either using information from the physical (PHY) layer or using information from the media access control (MAC) layer of the radio system.
Poincaré Embeddings for Learning Hierarchical Representations

Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this property. For this purpose, we introduce a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space – or more precisely into an n-dimensional Poincaré ball. Due to the underlying hyperbolic geometry, this allows us to learn parsimonious representations of symbolic data by simultaneously capturing hierarchy and similarity. We introduce an efficient algorithm to learn the embeddings based on Riemannian optimization and show experimentally that Poincaré embeddings outperform Euclidean embeddings significantly on data with latent hierarchies, both in terms of representation capacity and in terms of generalization ability.
Fiscal Volatility Shocks and Economic Activity ∗

We study the effects of changes in uncertainty about future fiscal policy on aggregate economic activity. In light of large fiscal deficits and high public debt levels in the U.S., a fiscal consolidation seems inevitable. However, there is notable uncertainty about the policy mix and timing of such a budgetary adjustment. To evaluate the consequences of the increased uncertainty, we first estimate tax and spending processes for the U.S. that allow for timevarying volatility. We then feed these processes into an otherwise standard New Keynesian business cycle model calibrated to the U.S. economy. We find that fiscal volatility shocks can have a sizable adverse effect on economic activity.
Impact of authentic leadership on performance : Role of followers ’ positive psychological capital and relational processes

Authentic leadership has received considerable attention and research support over the past decade. Now the time has come to refine and better understand how it impacts performance. This study investigates the moderating role followers’ positive psychological capital (PsyCap) and the mediating role that leader– member exchange (LMX) may play in influencing the relationship between authentic leadership and followers’ performance. Specifically, we tested this mediated moderation model with matched data from 794 followers and their immediate leaders. We found that authentic leadership is positively related to LMX and consequently followers’ performance, and to a larger degree, among followers who have low rather than high levels of PsyCap. Our discussion highlights the benefits of understanding the roles of relational processes and followers’ positive psychological resources involved in the effectiveness of authentic leadership and how they can be practically implemented.
The structure and dynamics of multilayer networks

In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporalor context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.  E-mail address: stefano.boccaletti@gmail.com (S. Boccaletti). http://dx.doi.org/10.1016/j.physrep.2014.07.001 0370-1573/ (2014) 1–122
Birth Preparedness and Its Association with Skilled Birth Attendance and Postpartum Checkups among Mothers in Gibe Wereda, Hadiya Zone, South Ethiopia

Birth preparedness program was designed to enhance skilled birth attendance and postpartum checkups of women in a developing country to reduce the three delays that lead women and neonates to death and disability. However, the relationship between birth preparedness with skilled birth attendance and postpartum checkups among mothers is not well studied. Therefore this study is intended to assess the association between birth preparedness and skilled birth attendance and postpartum checkups.  A community based cross-sectional study was conducted from March to April 2014. Eight out of 22 Kebeles were selected using probability proportional to size sampling method. Seven hundred and forty-five mothers were selected randomly from the sampling frame, generated from family folders obtained from health posts. Data was collected using pretested questionnaire by face-to-face interview. Data was entered into EpiData version 3.1 database and analyzed by SPSS version 16.  Out of 745 sampled mothers 728 (97.7%) participated in the study. One hundred and twelve (15.4%) and 128 (17.6%) mothers got skilled birth attendance and received postpartum checkups for their last child, respectively. Birth preparedness, educational status of women and their husbands, and antenatal care visits of mothers were found to be predictor of skilled birth attendance and postpartum checkups. Mothers well prepared for child birth were 6.7 times more likely to attend skilled birth attendance [AOR = 6.7 (2.7-16.4)] and 3 times more likely to follow postpartum checkups [AOR = 3.0 (1.5-5.9)] than poorly prepared mothers, respectively. Travel time to reach the nearest health facility was found as predictor for postpartum checkups of mothers; mothers who travel ≤ 2 hours were three times more likely to follow postpartum checkups than mothers who travel > 2 hours (AOR (95% CI) = 3.4 (1.5-7.9)).  Skilled birth attendance and postpartum checkups were low. Encouraging women to attend recommended antenatal care visits and providing advice and education on birth preparedness and obstetric danger signs are important interventions to increase skilled birth attendance and postpartum checkups.
An Experiment in Crowdfunding: Assessing the Role and Impact of Transaction-Level Information Controls

Crowdfunding has received a great deal of attention of late, as a promising avenue to fostering entrepreneurship and innovation. A notable aspect of shifting these financial activities to an online setting is that this brings increased visibility and traceability to a potentially sensitive activity. Most crowdfunding platforms maintain a public record of transactions, though a good number also provide transaction-level information controls, enabling users to conceal information as they see fit. We explore the impact of these information control mechanisms on crowdfunder behavior, acknowledging possible positive (e.g., comfort) and negative (e.g., privacy priming) impacts, employing a randomized experiment at a leading crowdfunding platform. Reducing access to information controls increases conversion rates (by ~5%), yet it also decreases average contribution amounts ($5.81 decline). We offer interpretations for these effects along with some empirical support, and we discuss implications for crowdfunding platform design.
Cloud computing security issues and challenges

Cloud computing has raised IT to newer limits by contribution the market environment data storage and volume with springy scalable computing processing power to match elastic demand supply, with reducing capital expenditure. Usually cloud computing services are delivered by a third party provider who owns the infrastructure. Cloud computing offers an innovative business model for organizations to adopt IT services without upfront investment. Security is one of the major issues which hamper the growth of cloud. Today, leading players, such as Amazon, Google, IBM, Microsoft and salesforce.com offer their cloud infrastructure for services.
Operation of Compressor and Electronic Expansion Valve via Different Controllers

The most critical problem in the world is to meet the energy demand, because of steadily increasing energy consumption. Refrigeration systems` electricity consumption has big portion in overall consumption. Therefore, considerable attention has been given to refrigeration capacity modulation system in order to decrease electricity consumption of these systems. Capacity modulation is used to meet exact amount of load at partial load and lowered electricity consumption by avoiding over capacity using. Variable speed refrigeration systems are the most common capacity modulation method for commercially and household purposes. Although the vapor compression refrigeration designed to satisfy the maximum load, they work at partial load conditions most of their life cycle and they are generally regulated as on/off controlled. The experimental chiller system contains four main components: compressor, condenser, expansion device, and evaporator in Fig.1 where this study deals with effects of different control methods on variable speed compressor (VSC) and electronic expansion valve (EEV). This chiller system has a scroll type VSC and a stepper motor controlled EEV.
Data and Models for Statistical Parsing with Combinatory Categorial Grammar

This dissertation is concerned with the creation of training data and the development of probability models for statistical parsing of English with Combinatory Categorial Grammar (CCG). Parsing, or syntactic analysis, is a prerequisite for semantic interpretation, and forms therefore an integral part of any system which requires natural language understanding. Since almost all naturally occurring sentences are ambiguous, it is not sufficient (and often impossible) to generate all possible syntactic analyses. Instead, the parser needs to rank competing analyses and select only the most likely ones. A statistical parser uses a probability model to perform this task. I propose a number of ways in which such probability models can be defined for CCG. The kinds of models developed in this dissertation, generative models over normal-form derivation trees, are particularly simple, and have the further property of restricting the set of syntactic analyses to those corresponding to a canonical derivation structure. This is important to guarantee that parsing can be done efficiently. In order to achieve high parsing accuracy, a large corpus of annotated data is required to estimate the parameters of the probability models. Most existing wide-coverage statistical parsers use models of phrase-structure trees estimated from the Penn Treebank, a 1-million-word corpus of manually annotated sentences from the Wall Street Journal. This dissertation presents an algorithm which translates the phrase-structure analyses of the Penn Treebank to CCG derivations. The resulting corpus, CCGbank, is used to train and test the models proposed in this dissertation. Experimental results indicate that parsing accuracy (when evaluated according to a comparable metric, the recovery of unlabelled word-word dependency relations), is as high as that of standard Penn Treebank parsers which use similar modelling techniques. Most existing wide-coverage statistical parsers use simple phrase-structure grammars whose syntactic analyses fail to capture long-range dependencies, and therefore do not correspond to directly interpretable semantic representations. By contrast, CCG is a grammar formalism in which semantic representations that include long-range dependencies can be built directly during the derivation of syntactic structure. These dependencies define the predicate-argument structure of a sentence, and are used for two purposes in this dissertation: First, the performance of the parser can be evaluated according to how well it recovers these dependencies. In contrast to purely syntactic evaluations, this yields a direct measure of how accurate the semantic interpretations returned by the parser are. Second, I propose a generative model that captures the local and non-local dependencies in the predicate-argument structure, and investigate the impact of modelling non-local in addition to local dependencies.
Automatic Musical Genre Classification of Audio Signals

Musical genres are categorical descriptions that are used to describe music. They are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. Genre categorization for audio has traditionally been performed manually. A particular musical genre is characterized by statistical properties related to the instrumentation, rhythmic structure and form of its members. In this work, algorithms for the automatic genre categorization of audio signals are described. More specifically, we propose a set of features for representing texture and instrumentation. In addition a novel set of features for representing rhythmic structure and strength is proposed. The performance of those feature sets has been evaluated by training statistical pattern recognition classifiers using real world audio collections. Based on the automatic hierarchical genre classification two graphical user interfaces for browsing and interacting with large audio collections have been developed.